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The Mobile Laser Scanning (MLS) data inevitably includes dynamic objects because there are always other vehicles (e.g., other cars, motorbikes, bikes, etc.) moving in the area near the MLS data collection vehicle on the road. These dynamic objects need to be removed in advance for many point cloud applications. This paper designs an efficient and memory-friendly data frame aware optimized Octomap-based dynamic object detection and removal method for MLS data. Firstly, the input MLS data is split into multiple data frames based on the timestamp. Each data frame is inserted into a separate Octomap with part of its neighbouring data frames. A statistics-based method is applied to each data frame to find the passable voxel cell space (free space) in Octomap and all points in the free space are extracted as free points. Second, the region of interest (ROI) related to the dynamic object is delineated to retain free points related to dynamic objects. Then the free-point rate and the multi-return rate are calculated to further remove noise and vegetation points from free points. Finally, the fixed radius search is used to extract dynamic objects from the filtered free points. The proposed method is tested in four case sites in Delft, the Netherlands. Results show that 84.98% of dynamic objects are detected and extracted correctly. The proposed method is 18.27% more efficient on average than the original Octomap method, can be further accelerated by parallel computing, and only needs 39.40% of the maximum memory consumption. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) is rapidly increasing in popularity for the capture and quantification of vegetation structure in natural landscapes. However, understanding the optimal implementation for forest capture in the field is still limited, particularly regarding the choice in acquisition path and length followed during data capture. How variable is the characterisation of vegetation structure when using different acquisition paths, and is this difference likely to be significant for most users? In this study we compared four acquisition path designs commonly cited in the literature to determine the importance of this choice for users when capturing vegetation structure. MLS point clouds were systematically captured to repeatedly survey study plots in a closed-canopy forest ecosystem in south-eastern Australia. Digital elevation models, canopy height models, and vertical voxel occupancy profiles were derived to illustrate the sensitivity of the path's length and configuration to variability in the quantification of vegetation structure. We found strong agreement between the acquisition path designs that increased as path length increased. No significant differences were found in digital elevation models derived at 75% and 100% path lengths. However, differences were observed in two and four of the 25% and 50% path length plots respectively. Significant differences in canopy height model data were only found in one plot at the 50% path length. Mean differences to the reference digital elevation models was 0.09 m across all plots, and 0.4 m for the canopy height models. Voxel occupancy profiles showed the greatest agreement between the understory and canopy where the mean difference between acquisition paths across all plots was 5.72%. Our findings have significant implications for the use of MLS from an operational perspective, as they illustrate the variability and reliability of their use in natural forested landscapes. Users can choose to balance their choice in acquisition path design with path length to meet requirements such as efficiency or ease of data capture in the field. • Terrestrial mobile laser scanning acquisition path designs and lengths were compared. • Acquisition path design did not significantly affect vegetation structure characterisation. • Shorter path lengths significantly influenced the capture of vegetation structure. [ABSTRACT FROM AUTHOR]

Street tree inventory is an important part of urban forest inventory. Mobile laser scanning (MLS) technology has a strong data acquisition ability inside the canopy and the trunk and is suitable for parameter estimation at the tree level. The first and key step is to segment individual trees from the street MLS data, which is a challenging problem due to the semantic gap. This paper proposes an efficient and effective method for street tree segmentation from MLS data using deep learning-based image instance segmentation. First, the three-dimensional (3D) street point cloud captured by the MLS system is mapped to a two-dimensional (2D) RGB image. Next, pixelwise tree proposals are segmented from the street image by a trained deep learning-based image instance segmentation model. Then, the 2D segmentation mask is mapped back to the 3D street point cloud to generate pointwise tree proposals. Finally, the proposals are optimized to obtain the final results. To evaluate and verify algorithm performance, an MLS point cloud is collected from a 1481.8 m-long one-side urban street containing various objects. Three deep learning-based image instance segmentation algorithms, YOLACT, BlendMask, and YOLOv8, are carried out, and YOLOv8 achieves the best results in terms of both accuracy and speed. YOLOv8 has the highest segmentation accuracy, with IoU= 0.85:0.05:0.95, with an average segmentation time of 26 ms per image. In the experiments comparing the algorithms to the existing hierarchical segmentation and classification segmentation methods, the proposed method outperforms the other two methods in accuracy and is faster. The precision is 0.9988, the recall is 0.9986, the F 1 score is 0.9987, and the time per scanline is 4.05 ms. Moreover, the proposed method can be applied to MLS data with a broad range of resolutions by introducing image resizing. [ABSTRACT FROM AUTHOR]

• An effective urban scene laser point cloud precise registration method. • Explores the technical details of pole-like objects as registration primitives. • MLS&BLS registration provide detailed representation of urban environments. • Fusion of multi-source laser data contributes to the construction of smart cities. In consideration of the significant changes in urban environmental scenes and the impact of factors such as signal obstruction on positioning, the spatial positions of multi-temporal Mobile Laser Scanning (MLS) point clouds collected in the same area are inconsistent, which makes it challenging to provide a complete expression of the urban road environment. To address this issue, a precise registration algorithm based on pole-like targets is proposed. The core steps include registration primitive extraction: a cylindrical spatial neighborhood is established based on the distinctive key points, and precise extraction of pole-like geospatial point cloud data is performed using the Euclidean clustering method with the normal curvature constraint. Achieving optimal spatial transformation: a circle center fitting method with additional Mean Absolute Error (MAE) loss function constraints was developed based on the stable elemental unit which was utilized to accurately construct homonymous feature point pairs. Validation results from multiple real urban point cloud datasets collected from different experimental areas and platforms demonstrate that our method achieves superior registration performance, with mean residuals below 5 cm in both the planar and elevation directions. The proposed method exhibits strong universality and registration robustness for multi-temporal point cloud data of the homologous or cross-sources. [ABSTRACT FROM AUTHOR]

The objective of the work is to assess the advantages of the laser scanning system in the topographical surveys. The analysis and assessment of two methods, the classical total station method and mobile laser scanning are presented in the article. The results of the performed investigations have been compared, the technological characteristics and accuracy of the investigations have been presented, as well as the procedure of the topographic image formation, possibilities and efficiency have been assessed. The real-life topographic surveys projects on the analysis of roads and streets (components) have been used where the ground surface, the components of the roads and the surrounding objects have been analysed. The analysis provides information on the availability and potential of the investigated methods and the final attained accuracy due to a certain number of the control points. The obtained results indicate that the main differences of the methods revealed when compiling the topographical images for urban or rural areas are the speed of measurements and data processing, level of detail of the results and various possibilities of the method implementation. [ABSTRACT FROM AUTHOR]

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Abstract: We evaluated the accuracy and efficiency of airborne (ALS), terrestrial (TLS) and mobile laser-scanning (MLS) methods that can be utilized in urban tree mapping and monitoring. In the field, 438 urban trees located in park and forested environments were measured and mapped from our study area located in Seurasaari, Helsinki, Finland. A field reference was collected, using a tree map created manually from TLS data. The tree detection rate and location accuracy were evaluated, using automatic or semiautomatic ALS individual tree detection (ALSITDauto or ALSITDvisual) and manual or automatic measurements of TLS and MLS (TLSauto, MLSauto, MLSmanual, MLSsemi). Our results showed that the best methods for tree detection were TLSauto and MLSmanual, which detected 73.29% and 79.22% of the reference trees, respectively. The location accuracies (RMSE) varied between 0.44m and 1.57m; the methods listed from the most accurate to most inaccurate were MLSsemi, TLSauto, MLSmanual, MLSauto, ALSITDauto and ALSITDvisual. We conclude that the accuracies of TLS and ALS were applicable for operational urban tree mapping in heterogeneous park forests. MLSmanual shows high potential but manual measurements are not feasible in operational tree mapping. Challenges that should be solved in further studies include ALSITDauto oversegmentation as well as MLSauto processing methodologies and data collection for tree detection. [Copyright &y& Elsevier]

Given that, in the recent years, the technique and technology has an impressive development and the topographic measurement equipment became increasingly sophisticated, the 3D laser scanning technology is the latest technology in the field, revolutionizing the topographic measurement technique. This technology allows solving problems that traditional methods would require excessive costs or it would be impossible to fulfill. Each scanning operation generates a "point cloud", points whose position is defined by the (x, y, z) coordinates in the scanner's own coordinate system. Since the laser scanning system is equipped with a navigation system (INS/GPS) the points coordinates will be defined in the WGS 84 coordinate system. [ABSTRACT FROM AUTHOR]