Search

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]

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]

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]

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]

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]

• 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]

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

• Point cloud processing based on Lab space colour. • Detection and correction of sky-coloured points caused by calibration errors. • Detection of sky-coloured points in eight real case studies with an average 94.7% F1-score. • Deviation from true colour of 0.976 ± 1.280 intensity levels tested on point clouds with synthetic errors. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]