128 results on '"mobile mapping systems"'
Search Results
2. Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data.
- Author
-
Cheng, Yi-Ting, Shin, Young-Ha, Shin, Sang-Yeop, Koshan, Yerassyl, Hodaei, Mona, Bullock, Darcy, and Habib, Ayman
- Subjects
- *
LIDAR , *LASER based sensors , *OPTICAL radar , *DATA mapping , *WEB portals - Abstract
The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. 3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds.
- Author
-
Suleymanoglu, Baris, Soycan, Metin, and Toth, Charles
- Subjects
- *
POINT cloud , *LEARNING strategies , *MACHINE learning , *LIDAR , *SPATIAL resolution , *DRIVERLESS cars , *AUTONOMOUS vehicles - Abstract
The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e.
- Author
-
Alsadik, Bashar
- Subjects
LIDAR ,POINT cloud ,INERTIAL navigation systems ,QUALITY control ,QUALITY assurance - Abstract
Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data
- Author
-
Yi-Ting Cheng, Young-Ha Shin, Sang-Yeop Shin, Yerassyl Koshan, Mona Hodaei, Darcy Bullock, and Ayman Habib
- Subjects
lane marking inventory ,lane marking extraction ,LiDAR ,image ,visualization/reporting ,mobile mapping systems ,Science - Abstract
The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%.
- Published
- 2024
- Full Text
- View/download PDF
6. 3D Road Boundary Extraction Based on Machine Learning Strategy Using LiDAR and Image-Derived MMS Point Clouds
- Author
-
Baris Suleymanoglu, Metin Soycan, and Charles Toth
- Subjects
mobile mapping systems ,mobile laser scanning ,curb detection ,3D road extraction ,machine learning ,Chemical technology ,TP1-1185 - Abstract
The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.
- Published
- 2024
- Full Text
- View/download PDF
7. Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings.
- Author
-
Sjölander, Andreas, Belloni, Valeria, Ansell, Anders, and Nordström, Erik
- Subjects
- *
TUNNEL lining , *TUNNELS , *INSPECTION & review , *CONVOLUTIONAL neural networks , *COMPUTER vision , *INTELLIGENT transportation systems , *TECHNOLOGICAL innovations - Abstract
In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system's efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Multi feature-rich synthetic colour to improve human visual perception of point clouds.
- Author
-
Balado, Jesús, González, Elena, Rodríguez-Somoza, Juan L., and Arias, Pedro
- Subjects
- *
POINT cloud , *VISUAL perception , *COLOR , *MACHINE learning , *FEATURE selection , *VISUALIZATION , *DESCRIPTOR systems - Abstract
Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
9. Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings
- Author
-
Justin A. Mahlberg, Yi-Ting Cheng, Darcy M. Bullock, and Ayman Habib
- Subjects
LiDAR ,retroreflectometer ,mobile mapping systems ,pavement markings ,retroreflectivity ,intensity profile ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.
- Published
- 2021
- Full Text
- View/download PDF
10. Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments.
- Author
-
Maté-González, Miguel Ángel, Di Pietra, Vincenzo, and Piras, Marco
- Subjects
- *
LIDAR , *CULTURAL property , *DOPPLER lidar , *AIRBORNE lasers , *OPTICAL scanners , *POINT cloud , *DOCUMENTATION - Abstract
In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. Comprehensive Generation of Historical Construction CAD Models from Data Provided by a Wearable Mobile Mapping System: A Case Study of the Church of Adanero (Ávila, Spain).
- Author
-
Rodríguez-Martín, Manuel, Sánchez-Aparicio, Luis Javier, Maté-González, Miguel Ángel, Muñoz-Nieto, Ángel Luis, and Gonzalez-Aguilera, Diego
- Subjects
- *
BUILDING information modeling , *POINT cloud , *DATA modeling , *CONSERVATION & restoration , *COMPUTER-aided design - Abstract
This paper presents the results of a complex three-dimensional reconstruction of the church of Nuestra Señora de la Asunción (Ávila, Spain) as an example of a successful process of verticalization from point clouds to a comprehensive computer-aided design (CAD) model. The reconstruction was carried out using the novel and advanced wearable mobile mapping system ZEB-REVO in combination with a lifting pole, in order to cover the whole geometry of the temple and, also, to model the different constructive elements. To this end, a set of good practices was followed, which allowed for passing from reality to the CAD model, such as the use of closed loops or even the use of different parametric and non-parametric strategies to capture the real geometry of the elements. As a result, this paper outlines the main guidelines for passing from point clouds to comprehensive CAD models, the former being useful for the application of smart preventive conservation processes, heritage building information models or even advanced numerical simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
12. Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation
- Author
-
Ankit Patel, Yi-Ting Cheng, Radhika Ravi, Yi-Chun Lin, Darcy Bullock, and Ayman Habib
- Subjects
LiDAR ,mobile mapping systems ,lane marking ,U-net ,transfer learning ,fine-tuning ,Geology ,QE1-996.5 - Abstract
Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization.
- Published
- 2021
- Full Text
- View/download PDF
13. Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings
- Author
-
Andreas Sjölander, Valeria Belloni, Anders Ansell, and Erik Nordström
- Subjects
automation ,mobile mapping systems ,tunnel inspection ,tunnel assessment ,tunnel concrete damage ,Chemical technology ,TP1-1185 - Abstract
In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.
- Published
- 2023
- Full Text
- View/download PDF
14. Performance Assessment of Mobile Laser Scanning Systems Using Velodyne Hdl-32e.
- Author
-
Alsadik, Bashar
- Subjects
LIDAR ,CARTOGRAPHIC services ,INERTIAL navigation systems ,QUALITY assurance ,SIMULATION methods & models - Abstract
Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
15. Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments
- Author
-
Miguel Ángel Maté-González, Vincenzo Di Pietra, and Marco Piras
- Subjects
LiDAR ,terrestrial laser scanner ,mobile mapping systems ,airborne LiDAR sensor ,unmanned aerial systems ,cultural heritage ,Chemical technology ,TP1-1185 - Abstract
In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance.
- Published
- 2022
- Full Text
- View/download PDF
16. Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds.
- Author
-
González-Collazo, Silvia María, Balado, Jesús, Garrido, Iván, Grandío, Javier, Rashdi, Rabia, Tsiranidou, Elisavet, del Río-Barral, Pablo, Rúa, Erik, Puente, Iván, and Lorenzo, Henrique
- Subjects
- *
POINT cloud , *DEEP learning , *URBAN research , *LASERS - Abstract
• Santiago Urban Dataset SUD was acquired with MLS and HMLS equipment. • Occlusions are reduced by the integration of MLS and HMLS data. • Point clouds are labelled by heuristic and Deep Learning methods in eight classes. • SUD is valid for comparing semantic segmentation works. • SUD is valid for urban 3D mobility studies. Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles , and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
17. Comprehensive Generation of Historical Construction CAD Models from Data Provided by a Wearable Mobile Mapping System: A Case Study of the Church of Adanero (Ávila, Spain)
- Author
-
Manuel Rodríguez-Martín, Luis Javier Sánchez-Aparicio, Miguel Ángel Maté-González, Ángel Luis Muñoz-Nieto, and Diego Gonzalez-Aguilera
- Subjects
heritage ,mobile mapping systems ,wearable mobile mapping system ,point clouds ,3D models ,Chemical technology ,TP1-1185 - Abstract
This paper presents the results of a complex three-dimensional reconstruction of the church of Nuestra Señora de la Asunción (Ávila, Spain) as an example of a successful process of verticalization from point clouds to a comprehensive computer-aided design (CAD) model. The reconstruction was carried out using the novel and advanced wearable mobile mapping system ZEB-REVO in combination with a lifting pole, in order to cover the whole geometry of the temple and, also, to model the different constructive elements. To this end, a set of good practices was followed, which allowed for passing from reality to the CAD model, such as the use of closed loops or even the use of different parametric and non-parametric strategies to capture the real geometry of the elements. As a result, this paper outlines the main guidelines for passing from point clouds to comprehensive CAD models, the former being useful for the application of smart preventive conservation processes, heritage building information models or even advanced numerical simulations.
- Published
- 2022
- Full Text
- View/download PDF
18. SISTEMA MOBILE MAPPING GEOSLAM ZEB HORIZON PER IL RILIEVO 3D DI UN POZZO
- Author
-
Simone Orlandini
- Subjects
Rilievo ,3D ,mobile mapping systems ,GEOslam ,zebhorizon ,Cartography ,GA101-1776 ,Cadastral mapping ,GA109.5 - Abstract
MicroGeo had the opportunity to experiment with the ZEB Horizon instrument to acquire a piezometric well in 3D. The scenario is not the easiest to detect. This type of wells, in fact, extends in depth for several tens of meters and with traditional scanning systems it is practically impossible to obtain a complete 3D acquisition of the subject. With the ZEB Horizon Mobile Mapping System it was possible to acquire the well. The subject presents itself as a 30 m deep piezometric well with a diameter of 3.6 m, with a high difficulty of acquisition: the extremely regular surfaces of the well have made the survey activity with the SLAM technique very complex, the algorithm that generates the 3D point cloud model must be extremely powerful in order to be able to generate a correct data even in a condition of extreme surface homogeneity.
- Published
- 2021
- Full Text
- View/download PDF
19. Automated Accuracy Assessment of a Mobile Mapping System with Lightweight Laser Scanning and MEMS Sensors.
- Author
-
Al-Durgham, Kaleel, Lichti, Derek D., Kwak, Eunju, Dixon, Ryan, and Balsi, Marco
- Subjects
OPTICAL scanners ,POINT cloud ,MICROELECTROMECHANICAL systems ,DRONE aircraft ,GEOSPATIAL data ,DETECTORS - Abstract
The accuracy assessment of mobile mapping system (MMS) outputs is usually reliant on manual labor to inspect the quality of a vast amount of collected geospatial data. This paper presents an automated framework for the accuracy assessment and quality inspection of point cloud data collected by MMSs operating with lightweight laser scanners and consumer-grade microelectromechanical systems (MEMS) sensors. A new, large-scale test facility has been established in a challenging navigation environment (downtown area) to support the analyses conducted in this research work. MMS point cloud data are divided into short time slices for comparison with the higher-accuracy, terrestrial laser scanner (TLS) point cloud of the test facility. MMS data quality is quantified by the results of registering the point cloud of each slice with the TLS datasets. Experiments on multiple land vehicle MMS point cloud datasets using a lightweight laser scanner and three different MEMS devices are presented to demonstrate the effectiveness of the proposed method. The mean accuracy of a consumer grade MEMS (<$100) was found to be 1.13 ± 0.47 m. The mean accuracy of two commercial MEMS (>$100) was in the range of 0.48 ± 0.23 m to 0.85 ± 0.52 m. The method presented here in can be straightforwardly implemented and adopted for the accuracy assessment of other MMSs types such as unmanned aerial vehicles (UAV)s. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
20. Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels.
- Author
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Al-Rawabdeh, Abdulla, Aldosari, Mohammed, Bullock, Darcy, and Habib, Ayman
- Subjects
REINFORCED soils ,WALLS ,WALL panels ,TRANSPORTATION corridors ,LIDAR ,DATA acquisition systems - Abstract
Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
21. Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme.
- Author
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Chiang, K.W., Tsai, G.J., Chang, H.W., Joly, C., and EI-Sheimy, N.
- Subjects
- *
GLOBAL Positioning System , *ERRORS-in-variables models , *NAUTICAL charts , *INERTIAL navigation systems - Abstract
Highlights • The proposed method is capable of giving stable navigation and mapping solutions. • Position accuracy is around 2 m in long GNSS (more than 300 s) outage. • The mapping results achieve the meter-level accuracy. • An approximately 60% improvement of long GNSS-denied experiments is achieved. Abstract Mobile Mapping Systems (MMS) with Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) and mapping sensors have been widely developed in recent years. However current systems and results are still prone to errors, especially in GNSS-denied or multipath environments. To provide robust and stable navigation information, particularly for mapping in long-term GNSS-denied environments, we propose a semi-tightly coupled integration scheme which integrates INS/GNSS with grid-based Simultaneous Localization and Mapping (SLAM). Although traditional SLAM using LiDAR can map the GNSS-denied environment efficiently, it is only in local localization. The proposed integration scheme is based on the Extended Kalman Filter (EKF) with motion constraints. In this scheme, a measurement model for grid-based SLAM is aided by the heading and velocity information. A special innovation of this scheme is the improved fusion of GNSS/INS with the use of grid-based SLAM serves like virtual odometer and virtual compass, thus gaining reliable measurements and error models to maintain good performance during INS-only mode. In addition, the initial values for example position and heading, are given to solve global localization and loop closure problems in SLAM. Finally, a smoothing and multi-resolution map strategy are applied offline to increase the robustness and performance of the proposed grid-based SLAM. Evaluation based on experimental data shows the significant improvement by the proposed semi-tightly coupled integration scheme with low-cost INS/GNSS and LiDAR, which is able to achieve 1–2 m' accuracy in terms of positioning and mapping. An approximately 60% improvement was achieved during long-term GNSS-denied environments using the proposed integration scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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- View/download PDF
22. A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation.
- Author
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González-Collazo, Silvia María, Balado, Jesús, González, Elena, and Nurunnabi, Abdul
- Subjects
- *
DEEP learning , *SUPERVISED learning , *POINT cloud , *CLASS differences , *LASERS - Abstract
Labelled point clouds are crucial to train supervised Deep Learning (DL) methods used for semantic segmentation. The objective of this research is to quantify discordances between the labels made by different people in order to assess whether such discordances can influence the success rates of a DL based semantic segmentation algorithm. An urban point cloud of 30 m road length in Santiago de Compostela (Spain) was labelled two times by ten persons. Discordances and its significance in manual labelling between individuals and rounds were calculated. In addition, a ratio test to signify discordance and concordance was proposed. Results show that most of the points were labelled accordingly with the same class by all the people. However, there were many points that were labelled with two or more classes. Class curb presented 5.9% of discordant points and 3.2 discordances for each point with concordance by all people. In addition, the percentage of significative labelling differences of the class curb was 86.7% comparing all the people in the same round and 100% comparing rounds of each person. Analysing the semantic segmentation results with a DL based algorithm, PointNet++, the percentage of concordance points are related with F-score value in R2 = 0.765, posing that manual labelling has significant impact on results of DL-based semantic segmentation methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
23. Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks
- Author
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Jesús Balado, Pedro Arias, Henrique Lorenzo, and Adrián Meijide-Rodríguez
- Subjects
mobile laser scanning ,mobile mapping systems ,LiDAR ,occlusions ,point density ,noise ,Science - Abstract
Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.
- Published
- 2021
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24. New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping
- Author
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Yi-Chun Lin, Tian Zhou, Taojun Wang, Melba Crawford, and Ayman Habib
- Subjects
orthophoto ,precision agriculture ,digital surface model ,unmanned aerial vehicles (UAV) ,mobile mapping systems ,flowering date ,Science - Abstract
Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.
- Published
- 2021
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- View/download PDF
25. Automated Accuracy Assessment of a Mobile Mapping System with Lightweight Laser Scanning and MEMS Sensors
- Author
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Kaleel Al-Durgham, Derek D. Lichti, Eunju Kwak, and Ryan Dixon
- Subjects
mobile mapping systems ,lightweight lidar ,accuracy assessment ,registration ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
The accuracy assessment of mobile mapping system (MMS) outputs is usually reliant on manual labor to inspect the quality of a vast amount of collected geospatial data. This paper presents an automated framework for the accuracy assessment and quality inspection of point cloud data collected by MMSs operating with lightweight laser scanners and consumer-grade microelectromechanical systems (MEMS) sensors. A new, large-scale test facility has been established in a challenging navigation environment (downtown area) to support the analyses conducted in this research work. MMS point cloud data are divided into short time slices for comparison with the higher-accuracy, terrestrial laser scanner (TLS) point cloud of the test facility. MMS data quality is quantified by the results of registering the point cloud of each slice with the TLS datasets. Experiments on multiple land vehicle MMS point cloud datasets using a lightweight laser scanner and three different MEMS devices are presented to demonstrate the effectiveness of the proposed method. The mean accuracy of a consumer grade MEMS ($100) was in the range of 0.48 ± 0.23 m to 0.85 ± 0.52 m. The method presented here in can be straightforwardly implemented and adopted for the accuracy assessment of other MMSs types such as unmanned aerial vehicles (UAV)s.
- Published
- 2021
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- View/download PDF
26. Imagery Network Fine Registration by Reference Point Cloud Data Based on the Tie Points and Planes
- Author
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Mehrdad Eslami and Mohammad Saadatseresht
- Subjects
fine registration ,photogrammetric imagery ,laser scanner point cloud ,mobile mapping systems ,calibration ,Chemical technology ,TP1-1185 - Abstract
Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach.
- Published
- 2021
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- View/download PDF
27. Active use of panoramic mobile mapping systems for as built surveying and heritage documentation.
- Author
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Alsadik, Bashar and Khalid Jasim, Luma
- Subjects
ORTHOPHOTOGRAPHY ,CARTOGRAPHY ,PANORAMIC photography - Abstract
Mobile mapping systems (MMS) are widely used technology nowadays for spatial data collection of large scale projects like for city and highway mapping. The systems are mainly equipped with laser scanning sensors and/or imaging sensors mounted on a moving vehicle during the scene capture. Imaging sensors are normally cameras which either capture perspective or panoramic images covering the whole horizon of the vehicle. The orientation of the captured panoramic images is accurate to centimeters' level because of the precise positioning and navigation systems equipped with these mapping systems. However, the positioning accuracy of mobile mapping systems can be degraded in city centers or urban canyons because of the satellite signal disturbances. In this article, we discuss the following objectives: (1) the possibility to use the mobile mapping images for cultural heritage documentation and as built surveying and how accurate the mapping can be; (2) the concept of using the mobile mapping images as a tool of georeferencing the crowdsource images; and (3) the efficiency of using the multi-temporal mobile mapping images for occluded free cultural heritage facade orthophotos. The mobile mapping systems of CycloMedia with two panoramic products of Cyclorama images (12 MP) and HD Cycloramas (100 MP) are used for the experimental tests in this research article. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
28. Point clouds by SLAM-based mobile mapping systems: accuracy and geometric content validation in multisensor survey and stand-alone acquisition.
- Author
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Sammartano, Giulia and Spanò, Antonia
- Abstract
The paper provides some operative replies to evaluate the effectiveness and the critical issues of the simultaneous localisation and mapping (SLAM)-based mobile mapping system (MMS) called ZEB by GeoSLAM™ https://geoslam.com/technology/. In these last years, this type of handheld 3D mapping technology has increasingly developed the framework of portable solutions for close-range mapping systems that have mainly been devoted to mapping the indoor building spaces of enclosed or underground environments, such as forestry applications and tunnels or mines. The research introduces a set of test datasets related to the documentation of landscape contexts or the 3D modelling of architectural complexes. These datasets are used to validate the accuracy and informative content richness about ZEB point clouds in stand-alone solutions and in cases of combined applications of this technology with multisensor survey approaches. In detail, the proposed validation method follows the fulfilment of the endorsed approach by use of root mean square error (RMSE) evaluation and deviation analysis assessment of point clouds between SLAM-based data and 3D point cloud surfaces computed by more precise measurement methods to evaluate the accuracy of the proposed approach. Furthermore, this study specifies the suitable scale for possible handlings about these peculiar point clouds and uses the profile extraction method in addition to feature analyses such as corner and plane deviation analysis of architectural elements. Finally, because of the experiences reported in the literature and performed in this work, a possible reversal is suggested. If in the 2000s, most studies focused on intelligently reducing the light detection and ranging (LiDAR) point clouds where they presented redundant and not useful information, contrariwise, in this sense, the use of MMS methods is proposed to be firstly considered and then to increase the information only wherever needed with more accurate high-scale methods. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
29. Fully Automated Segmentation of 2D and 3D Mobile Mapping Data for Reliable Modeling of Surface Structures Using Deep Learning
- Author
-
Alexander Reiterer, Katharina Wäschle, Dominik Störk, Achim Leydecker, and Niko Gitzen
- Subjects
mobile mapping systems ,road surface texture ,supervised learning ,semantic segmentation ,broadband infrastructure ,Science - Abstract
Maintenance and expansion of transport and communications infrastructure requires ongoing construction work on a large scale. To plan and execute these in the best possible way, up-to-date and highly detailed digital maps are needed. For example, until recently, telecommunication companies have performed documentation and mapping of as-built urban structures for construction work manually and with great time expense. Mobile mapping systems offer a solution for documenting urban environments fast and mostly automated. In consequence, large amounts of recorded data emerge in short time, creating the need for automated processing and modeling of these data to provide reliable foundations for digital planning in reasonable time. We present (a) a procedure for fully automated processing of mobile mapping data for digital construction planning in the context of nationwide broadband network expansion and (b) an in-depth study of the performance of this procedure on real-world data. Our multi-stage pipeline segments georeferenced images and fuses segmentations with 3D data, which allows exact localization of surfaces and objects, which can then be passed via interface, e.g., to a geographic information system (GIS). The final system is able to distinguish between similar looking surfaces, such as concrete and asphalt, with a precision between 80% and 95%, regardless of setting or season.
- Published
- 2020
- Full Text
- View/download PDF
30. Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
- Author
-
Abdulla Al-Rawabdeh, Mohammed Aldosari, Darcy Bullock, and Ayman Habib
- Subjects
MSE walls ,smooth precast concrete panels ,mobile mapping systems ,mobile LiDAR ,static LiDAR ,performance/serviceability measures ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls.
- Published
- 2020
- Full Text
- View/download PDF
31. Intensity Thresholding and Deep Learning Based Lane Marking Extraction and Lane Width Estimation from Mobile Light Detection and Ranging (LiDAR) Point Clouds
- Author
-
Yi-Ting Cheng, Ankit Patel, Chenglu Wen, Darcy Bullock, and Ayman Habib
- Subjects
lane marking extraction ,lane width estimation ,intensity normalization ,deep learning ,mobile mapping systems ,automated labeling ,Science - Abstract
Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.
- Published
- 2020
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- View/download PDF
32. Ideal Angular Orientation of Selected 64-Channel Multi Beam Lidars for Mobile Mapping Systems
- Author
-
Bashar Alsadik
- Subjects
lidar ,point cloud density ,point cloud coverage ,mobile mapping systems ,3d simulation ,pandar64 ,ouster os-1-64 ,Science - Abstract
Lidar technology is thriving nowadays for different applications mainly for autonomous navigation, mapping, and smart city technology. Lidars vary in different aspects and can be: multi beam, single beam, spinning, solid state, full 360 field of view FOV, single or multi pulse returns, and many other geometric and radiometric aspects. Users and developers in the mapping industry are continuously looking for new released Lidars having high properties of output density, coverage, and accuracy while keeping a lower cost. Accordingly, every Lidar type should be well evaluated for the final intended mapping aim. This evaluation is not easy to implement in practice because of the need to have all the investigated Lidars available in hand and integrated into a ready to use mapping system. Furthermore, to have a fair comparison; it is necessary to ensure the test applied in the same environment at the same travelling path among other conditions. In this paper, we are evaluating two state-of-the-art multi beam Lidar types: Ouster OS-1-64 and Hesai Pandar64 for mapping applications. The evaluation of the Lidar types is applied in a simulation environment which approximates reality. The paper shows the determination of the ideal orientation angle for the two Lidars by assessing the density, coverage, and accuracy and presenting clear performance quantifications and conclusions.
- Published
- 2020
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- View/download PDF
33. Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units
- Author
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Radhika Ravi and Ayman Habib
- Subjects
lidar ,mobile mapping systems ,profile-based calibration ,targetless ,airborne and terrestrial mms ,Science - Abstract
LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS − two airborne and one terrestrial − with one or more LiDAR unit.
- Published
- 2020
- Full Text
- View/download PDF
34. Optimising Mobile Mapping System Laser Scanner Orientation
- Author
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Conor Cahalane, Paul Lewis, Conor P. McElhinney, and Timothy McCarthy
- Subjects
point density ,laser scanner ,scanner orientation ,mobile mapping systems ,Geography (General) ,G1-922 - Abstract
Multiple laser scanner hardware configurations can be applied to Mobile Mapping Systems. As best practice, laser scanners are rotated horizontally or inclined vertically to increase the probability of contact between the laser scan plane and any surfaces that are perpendicular to the direction of travel. Vertical inclinations also maximise the number of scan profiles striking narrow vertical features, something that can be of use when trying to recognise features. Adding a second scanner allows an MMS to capture more data and improve laser coverage of an area by filling in laser shadows. However, in any MMS the orientation of each scanner on the platform must be decided upon. Changes in the horizontal or vertical orientations of the scanner can increase the range to vertical targets and the road surface, with excessive scanner angles lowering point density significantly. Limited information is available to assist the manufacturers or operators in identifying the optimal scanner orientation for roadside surveys. The method proposed in this paper applies 3D surface normals and geometric formulae to assess the influence of scanner orientation on point distribution. It was demonstrated that by changing the orientation of the scanner the number of pulses striking a target could be greatly increased, and the number of profiles intersecting with the target could also be increased—something that is particularly important for narrow vertical features. The importance of identifying the correct trade-off between the number of profiles intersecting with the target and the point spacing was also raised.
- Published
- 2015
- Full Text
- View/download PDF
35. Advances in Mobile Mapping Technologies.
- Author
-
Lehtola, Ville, Goulette, François, Lehtola, Ville, and Nüchter, Andreas
- Subjects
History of engineering & technology ,Technology: general issues ,3D camera ,3D city model ,3D mapping ,3D simulation ,3D surveying ,6-DoF ,CAM localization ,CNN features ,DSIEKF ,DTM ,F-CNN ,Helmert variance component estimation ,IEKF ,LiDAR ,Lidar ,MSS ,Mobile Laser Scanning ,Ouster OS-1-64 ,Pandar64 ,RGB-D ,RetinaNet ,TLS reference point clouds ,V-SLAM ,accuracy ,boresight angles ,building 3D modelling ,configuration analysis ,control points ,controllability ,convolutional neural networks ,correlation coefficient ,data fusion ,dataset ,deep learning ,embedded-systems ,evaluation ,exposure control ,geometrical constraints ,georeferencing ,graph matching ,guidance ,imaging network design ,inception ,laser scanning ,lever arm ,line feature matching method ,loop closure detection ,manhole cover ,mobile laser scanning ,mobile mapping ,mobile mapping systems ,outdoor ,parking statistics ,path planning ,performance evaluation ,photogrammetry ,plane-based calibration field ,point and line features ,point cloud ,point cloud coverage ,point cloud density ,point clouds ,real-time ,robot operating system ,scene completion ,semantic ,semantic topology graph ,smart city ,synthetic ,transfer learning ,vehicle detection ,view planning ,visual SLAM ,visual-inertial odometry - Abstract
Summary: Mobile mapping is applied widely in society, for example, in asset management, fleet management, construction planning, road safety, and maintenance optimization. Yet, further advances in these technologies are called for. Advances can be radical, such as changes to the prevailing paradigms in mobile mapping, or incremental, such as the state-of-the-art mobile mapping methods. With current multi-sensor systems in mobile mapping, laser-scanned data are often registered in point clouds with the aid of global navigation satellite system (GNSS) positioning or simultaneous localization and mapping (SLAM) techniques and then labeled and colored with the aid of machine learning methods and digital camera data. These multi-sensor platforms are beginning to undergo further advancements via the addition of multi-spectral and other sensors and via the development of machine learning techniques used in processing this multi-modal data. Embedded systems and minimalistic system designs are also attracting attention, from both academic and commercial perspectives.This book contains the accepted publications of the Special Issue 'Advances in Mobile Mapping Technologies' of the Remote Sensing journal. It consists of works introducing a new mobile mapping dataset ('Paris CARLA 3D'), system calibration studies, SLAM topics, and multiple deep learning works for asset detection. We, the Guest Editors, Ville Lehtola from University of Twente, Netherlands, Andreas Nüchter from University of Würzburg, Germany, and François Goulette from Mines Paris- PSL University, France, wish to thank all the authors who contributed to this collection.
36. KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS.
- Author
-
Tsai, G. J., Chiang, K. W., and El-Sheimy, N.
- Subjects
CALIBRATION ,OPTICAL scanners ,OPTICAL radar - Abstract
More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo-referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDARSimultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
37. ENHANCING THE RESOLUTION OF URBAN DIGITAL TERRAIN MODELS USING MOBILE MAPPING SYSTEMS.
- Author
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Feng, Y., Brenner, C., and Sester, M.
- Subjects
DIGITAL elevation models ,DIGITAL mapping ,METROPOLITAN areas - Abstract
Digital Terrain Models (DTMs) are essential surveying products for terrain based analyses, especially for overland flow modelling. Nowadays, many high resolution DTM products are generated by Airborne Laser Scanning (ALS). However, DTMs with even higher resolution are of great interest for a more precise overland flow modelling in urban areas. With the help of mobile mapping techniques, we can obtain much denser measurements of the ground in the vicinity of roads. In this research, a study area in Hannover, Germany was measured by a mobile mapping system. Point clouds from 485 scan strips were aligned and a DTM was extracted. In order to achieve a product with completeness, this mobile mapping produced DTM was then merged and adapted with a DTM product with 0.5 m resolution from a mapping agency. Systematic evaluations have been conducted with respect to the height accuracy of the DTM products. The results show that the final DTM product achieved a higher resolution (0.1 m) near the roads while essentially maintaining its height accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
38. "TORINO 1911" PROJECT: A CONTRIBUTION OF A SLAM-BASED SURVEY TO EXTENSIVE 3D HERITAGE MODELING.
- Author
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Chiabrando, F., Coletta, C. Della, Sammartano, G., Spanò, A., and Spreafico, A.
- Subjects
THREE-dimensional modeling ,GEOMATICS ,DETECTORS - Abstract
In the framework of the digital documentation of complex environments the advanced Geomatics researches offers integrated solution and multi-sensor strategies for the 3D accurate reconstruction of stratified structures and articulated volumes in the heritage domain. The use of handheld devices for rapid mapping, both image- and range-based, can help the production of suitable easy-to use and easynavigable 3D model for documentation projects. These types of reality-based modelling could support, with their tailored integrated geometric and radiometric aspects, valorisation and communication projects including virtual reconstructions, interactive navigation settings, immersive reality for dissemination purposes and evoking past places and atmospheres. The aim of this research is localized within the "Torino 1911" project, led by the University of San Diego (California) in cooperation with the PoliTo. The entire project is conceived for multi-scale reconstruction of the real and no longer existing structures in the whole park space of more than 400,000m
2 , for a virtual and immersive visualization of the Turin 1911 International "Fabulous Exposition" event, settled in the Valentino Park. Particularly, in the presented research, a 3D metric documentation workflow is proposed and validated in order to integrate the potentialities of LiDAR mapping by handheld SLAM-based device, the ZEB REVO Real Time instrument by GeoSLAM (2017 release), instead of TLS consolidated systems. Starting from these kind of models, the crucial aspects of the trajectories performances in the 3D reconstruction and the radiometric content from imaging approaches are considered, specifically by means of compared use of common DSLR cameras and portable sensors. [ABSTRACT FROM AUTHOR]- Published
- 2018
- Full Text
- View/download PDF
39. MIMIC: An Innovative Methodology for Determining Mobile Laser Scanning System Point Density
- Author
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Conor Cahalane, Conor P. McElhinney, Paul Lewis, and Timothy McCarthy
- Subjects
point density ,mobile mapping systems ,performance ,LiDAR ,Science - Abstract
Understanding how various Mobile Mapping System (MMS) laser hardware configurations and operating parameters exercise different influence on point density is important for assessing system performance, which in turn facilitates system design and MMS benchmarking. Point density also influences data processing, as objects that can be recognised using automated algorithms generally require a minimum point density. Although obtaining the necessary point density impacts on hardware costs, survey time and data storage requirements, a method for accurately and rapidly assessing MMS performance is lacking for generic MMSs. We have developed a method for quantifying point clouds collected by an MMS with respect to known objects at specified distances using 3D surface normals, 2D geometric formulae and line drawing algorithms. These algorithms were combined in a system called the Mobile Mapping Point Density Calculator (MIMIC) and were validated using point clouds captured by both a single scanner and a dual scanner MMS. Results from MIMIC were promising: when considering the number of scan profiles striking the target, the average error equated to less than 1 point per scan profile. These tests highlight that MIMIC is capable of accurately calculating point density for both single and dual scanner MMSs.
- Published
- 2014
- Full Text
- View/download PDF
40. Calculation of Target-Specific Point Distribution for 2D Mobile Laser Scanners
- Author
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Conor Cahalane, Conor P. McElhinney, Paul Lewis, and Tim McCarthy
- Subjects
mobile mapping systems ,performance ,LiDAR ,Chemical technology ,TP1-1185 - Abstract
The current generation of Mobile Mapping Systems (MMSs) capture high density spatial data in a short time-frame. The quantity of data is difficult to predict as there is no concrete understanding of the point density that different scanner configurations and hardware settings will exhibit for objects at specific distances. Obtaining the required point density impacts survey time, processing time, data storage and is also the underlying limit of automated algorithms. This paper details a novel method for calculating point and profile information for terrestrial MMSs which are required for any point density calculation. Through application of algorithms utilising 3D surface normals and 2D geometric formulae, the theoretically optimal profile spacing and point spacing are calculated on targets. Both of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings-all important features in asset management surveys.
- Published
- 2014
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41. Realistic correction of sky-coloured points in Mobile Laser Scanning point clouds
- Author
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Elena González, Jesús Balado, Pedro Arias, and Henrique Lorenzo
- Subjects
LiDAR ,Coloured point cloud ,331102 Ingeniería de control ,Lab colour space ,02 engineering and technology ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials ,010309 optics ,Image processing ,11. Sustainability ,0103 physical sciences ,Mobile Mapping Systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,Point cloud processing - Abstract
The enrichment of the point clouds with colour images improves the visualisation of the data as well as the segmentation and recognition processes. Coloured point clouds are becoming increasingly common, however, the colour they display is not always as expected. Errors in the colouring of point clouds acquired with Mobile Laser Scanning are due to perspective in the camera image, different resolution or poor calibration between the LiDAR sensor and the image sensor. The consequences of these errors are noticeable in elements captured in images, but not in point clouds, such as the sky. This paper focuses on the correction of the sky-coloured points, without resorting to the images that were initially used to colour the whole point cloud. The proposed method consists of three stages. First the region of interest where the erroneously coloured points are accumulated, is selected. Second, the sky-coloured points are detected by calculating the colour distance in the Lab colour space to a sample of the sky-colour. And third, the colour of the sky-coloured detected points is restored from the colour of the nearby points. The method is tested in ten real case studies with their corresponding point clouds from urban and rural areas. In two case studies, sky-coloured points were assigned manually and the remaining eight case studies, the sky-coloured points are derived from the acquisition errors. The algorithm for sky-coloured points detection obtained an average F1-score of 94.7%. The results show a correct reassignment of colour, texture, and patterns, while improving the point cloud visualisation. Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Xunta de Galicia | Ref. ED481B-2019-061 Xunta de Galicia | Ref. ED431C 2020/01 Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43 Agencia Estatal de Investigación | Ref. PID2019-108816RB-I00
- Published
- 2022
42. Evaluation of Different LiDAR Technologies for the Documentation of Forgotten Cultural Heritage under Forest Environments
- Author
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VINCENZO DI PIETRA, Marco Piras, and Miguel Ángel Maté-González
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accuracy analysis ,LiDAR ,Data Collection ,Lasers ,terrestrial laser scanner ,mobile mapping systems ,airborne LiDAR sensor ,unmanned aerial systems ,cultural heritage ,point cloud analysis ,Documentation ,Forests ,Biochemistry ,Atomic and Molecular Physics, and Optics ,Analytical Chemistry ,Electrical and Electronic Engineering ,Instrumentation - Abstract
In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance.
- Published
- 2022
43. A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas.
- Author
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Weinmann, Martin, Weinmann, Michael, Mallet, Clément, and Brédif, Mathieu
- Subjects
- *
IMAGE segmentation , *FEATURE selection , *SEMANTICS , *ALGORITHMS , *LAPTOP computers , *CITIES & towns - Abstract
In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h). [ABSTRACT FROM AUTHOR]
- Published
- 2017
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- View/download PDF
44. DEVELOPING AN IMAGE BASED LOW-COST MOBILE MAPPING SYSTEM FOR GIS DATA ACQUISITION
- Author
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Dimitrios Skarlatos, E. Tournas, and E. Frentzos
- Subjects
lcsh:Applied optics. Photonics ,Digital image correlation ,Machine vision ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,010501 environmental sciences ,Civil Engineering ,01 natural sciences ,lcsh:Technology ,Data acquisition ,Computer vision ,0105 earth and related environmental sciences ,Sensor Integration ,Pixel ,Orientation (computer vision) ,business.industry ,lcsh:T ,010401 analytical chemistry ,lcsh:TA1501-1820 ,GIS ,0104 chemical sciences ,Photogrammetry ,Low Cost ,GNSS applications ,lcsh:TA1-2040 ,Mobile Mapping Systems ,Engineering and Technology ,Artificial intelligence ,business ,lcsh:Engineering (General). Civil engineering (General) ,Mobile mapping ,Camera resectioning ,Reference frame - Abstract
Presented at 24th ISPRS Congress - Technical Commission I, 2020, 31 August - 2 September Nice, Virtual, France The aim of this study is to develop a low-cost mobile mapping system (MMS) with the integration of vehicle-based navigation data and stereo images acquired along vehicle paths. The system consists of a dual frequency GNSS board combined with a low-cost INS unit and two machine vision cameras that collect colour image data for road and roadside objects. The navigation data and the image acquisition are properly synchronized to associate position and attitude to each digital frame captured. In this way, upon pixel location of objects appearing on the video frames, their absolute geographical coordinates can be extracted by employing standard photogrammetric methods. Several calibration steps are implemented before survey operation: camera calibration, relative orientation between cameras and determination of rotation angles and offsets between vehicle and cameras reference frames. A software tool has been developed to facilitate and speed up the calibration procedures. Furthermore, easy object coordinate extraction is supported, either in auto mode, where the conjugate image coordinates are obtained in real time using image correlation techniques. Several surveying experiments were executed to certify and check the accuracy and efficiency of the system. From the achieved results, the developed system is efficient for collecting and positioning road spatial objects such as such as road boundaries, traffic lights, road signs, power poles, etc, more rapidly and less expensively. The obtained absolute positional accuracy is less than 1 meter, depending on the availability and quality of the GPS signal.
- Published
- 2020
45. Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings
- Author
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Ayman Habib, Darcy M. Bullock, Justin Mahlberg, and Yi-Ting Cheng
- Subjects
LiDAR ,intensity profile ,Computer science ,mobile mapping systems ,pavement markings ,Engineering (General). Civil engineering (General) ,retroreflectivity ,Mobile lidar ,Lidar ,retroreflectometer ,Network level ,Lidar data ,Enhanced Data Rates for GSM Evolution ,TA1-2040 ,Linear correlation ,Intensity (heat transfer) ,Mobile mapping ,Remote sensing - Abstract
The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.
- Published
- 2021
- Full Text
- View/download PDF
46. Direct Sensor Orientation of a Land-Based Mobile Mapping System
- Author
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Yu-Hua Li, Yi-Hsing Tseng, Ki-In Bang, Kai-Wei Chiang, Ana P. Kersting, Ayman F. Habib, and Jiann-Yeou Rau
- Subjects
Mobile Mapping Systems ,direct sensor orientation ,camera calibration ,direct georeferencing ,mounting parameters ,Chemical technology ,TP1-1185 - Abstract
A land-based mobile mapping system (MMS) is flexible and useful for the acquisition of road environment geospatial information. It integrates a set of imaging sensors and a position and orientation system (POS). The positioning quality of such systems is highly dependent on the accuracy of the utilized POS. This limitation is the major drawback due to the elevated cost associated with high-end GPS/INS units, particularly the inertial system. The potential accuracy of the direct sensor orientation depends on the architecture and quality of the GPS/INS integration process as well as the validity of the system calibration (i.e., calibration of the individual sensors as well as the system mounting parameters). In this paper, a novel single-step procedure using integrated sensor orientation with relative orientation constraint for the estimation of the mounting parameters is introduced. A comparative analysis between the proposed single-step and the traditional two-step procedure is carried out. Moreover, the estimated mounting parameters using the different methods are used in a direct geo-referencing procedure to evaluate their performance and the feasibility of the implemented system. Experimental results show that the proposed system using single-step system calibration method can achieve high 3D positioning accuracy.
- Published
- 2011
- Full Text
- View/download PDF
47. Intelligent Sensor Positioning and Orientation Through Constructive Neural Network-Embedded INS/GPS Integration Algorithms
- Author
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Kai-Wei Chiang and Hsiu-Wen Chang
- Subjects
GPS/INS ,sensor integration ,mobile mapping systems ,constructive neural networks ,Chemical technology ,TP1-1185 - Abstract
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks––the Cascade Correlation Neural Network (CCNNs)––to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.
- Published
- 2010
- Full Text
- View/download PDF
48. An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors
- Author
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Yun-Wen Huang, Chia-Yuan Li, Hsiu-Wen Chang, and Kai-Wei Chiang
- Subjects
GPS ,INS ,Integration ,Mobile Mapping Systems ,Artificial Neural networks ,Chemical technology ,TP1-1185 - Abstract
Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated.
- Published
- 2009
- Full Text
- View/download PDF
49. Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation
- Author
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Radhika Ravi, Yi-Chun Lin, Darcy M. Bullock, Yi-Ting Cheng, Ankit Patel, and Ayman Habib
- Subjects
LiDAR ,010504 meteorology & atmospheric sciences ,Computer science ,0211 other engineering and technologies ,02 engineering and technology ,transfer learning ,01 natural sciences ,U-net ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,QE1-996.5 ,Artificial neural network ,intensity profile ,business.industry ,Deep learning ,Pattern recognition ,Geology ,mobile mapping systems ,lane marking ,Visualization ,Lidar ,RGB color model ,Artificial intelligence ,business ,Encoder ,fine-tuning ,Test data ,Mobile mapping - Abstract
Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization.
- Published
- 2021
- Full Text
- View/download PDF
50. INTERSEÇÃO FOTOGRAMÉTRICA EM UM BANCO DE IMAGENS GEORREFERENCIADAS
- Author
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João F. C. da Silva, Ricardo L. Barbosa, Rodrigo B. de A. Gallis, and Elivagner B. de Oliveira
- Subjects
Mobile Mapping Systems ,Photogrammetric Intersection ,Image Database ,Geography. Anthropology. Recreation ,Cartography ,GA101-1776 - Abstract
Mobile mapping systems (MMS) provide a sequence of image pairs that allow the measurement and analysis of photogrammetric point and features for topographic mapping purposes and also for a visual evaluation of some specific conditions of the roads. A project of MMS generates a large number of images that are stored in magnetic tapes (DVCAM) at 30 fps (frames per second) in the camera and then downloaded to a computer hard disk. Even selecting only one image per second, the number of images related to the traveled roads is still expressive. A georeferenced road image pair database was built in order to store and manage image data obtained by the MMS. A module of photogrammetric intersection was added to the database in order to measure the image pairs and to compute the spatial coordinates of the selected points of the roads and their vicinities. The results obtained from different methods of intersection are presented and discussed including an urban road map at scale 1:2000. As expected, the rigorous model of the collinearity equations provided the best results after using the estimates of the photogrammetric intersection by parameter grouping as approximate values for the three cartesian coordinates of the object points.
- Published
- 2003
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