Mobile surveying system for road assets monitoring and management

Luo, Xiang; Ren, Xiang; Li, Yan; Wang, Jianguo Jack

doi:10.1109/iciea.2012.6360997

Cited by 2 publications

(1 citation statement)

References 8 publications

(10 reference statements)

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“…As an alternative, lidar-based Simultaneous Localization and Mapping (SLAM) Mobile Laser Scanning Systems (MLS) can enable frequent, large-scale geotechnical monitoring due to significantly higher data acquisition efficiency than traditional inspections [15][16][17][18][19][20]. MLS can also offer safety benefits by removing operators from hazardous areas, as they can be integrated into autonomous robotic platforms such as quadruped robots or mining equipment [21].…”

Section: Introductionmentioning

confidence: 99%

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

et al. 2023

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Convergence and rockmass failure are significant hazards to personnel and physical assets in underground tunnels, caverns, and mines. Mobile Laser Scanning Systems (MLS) can deliver large volumes of point cloud data at a high frequency and on a large scale. However, current change detection approaches do not deliver sufficient sensitivity and precision for real-time performance on large-scale datasets. We present a novel, octree-based computational framework for intra-voxel statistical inference change detection and deformation analysis. Our approach exploits high-density MLS data to test for statistical significance for appearing objects caused by rockfall and for low-magnitude deformations, such as convergence. In field tests, our method detects rock falls with side lengths as small as 0.03 m and convergence as low as 0.01 m, or 0.5% wall-to-wall strain. When compared against a state-of-the-art multi-scale model-to-model cloud comparison (M3C2)-based method, ours is less sensitive to noisy data and parameter selection while also requiring fewer parameters. Most notably, our method is the only one tested that can perform real-time change detection on large-scale datasets on a single processor thread. Our method achieves a computational improvement of 50 times over single-threaded M3C2 while maintaining a performance scalability that is four times greater with dataset size. Our framework shows significant potential to enable accurate real-time geotechnical monitoring of large-scale underground spaces.

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Section: Introductionmentioning

confidence: 99%

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

et al. 2023

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Tunnel Modeling Using Mobile Mapping Lidar Points

Deshpande

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this paper, a method to model a tunnel using lidar points is presented. The data used was collected using Leica Pegasus Two Ultimate with a Z+F 9012 Profiler mounted on a mobile platform. The tunnel was approximately 151 m long. Visual inspection of a cross-section of the tunnel showed two rail tracks supported on ballast and sidewalks along both sidewalls of the tunnel. The walls and the ceiling of the tunnel were made of five planar surfaces. The tunnel alignment was straight, without any horizontal or vertical curves. The bearing of the central axis of the tunnel was N12.2oW. The following methodology was developed to model just the planar surfaces of the tunnel by excluding the rails, ballast, sidewalks, powerlines, and other accessories. The entire methodology was divided into three broad parts. In the first part, a model cross-section was created. Since the design plans of the tunnel were not available, the model cross-section polyline was created using mean tunnel dimensions from random cross-section points. The model cross-section consisted of the walls and the ceiling of the tunnel. Points were placed at every 1 cm along the model polyline. Six of the model points that represented the shape of the tunnel were selected as salient points. The lower-left salient point was considered as the seed point. In the second part, to define a reference axis of the tunnel, an approximate centerline was manually defined by selecting points at its start and end. Lidar points within 1 m at the start and the end of the tunnel were modeled using the model points to determine the centroids. The reference axis was determined by connecting the centroids at the start and the end of the tunnel. In the third part, the tunnel points were sliced along the reference axis at 5 cm intervals. The model cross-section was matched to points within each tunnel slice using a three-stage approach. In the first stage, the pattern of salient points was matched to the tunnel points by placing the seed point at every tunnel point location. The distances between salient points and their nearest tunnel points were calculated. Ten sets of tunnel points with the least differences to the salient points were shortlisted. In the second stage, a dense point-to-point matching was performed between the model and sliced tunnel data at the shortlisted points. The shortlisted point location with the least difference between the tunnel and the model points was considered as a match. At this point, the model points were hinged to the tunnel points at the seed point location. Hence, in the last stage, a six-parameter affine transformation was performed to match the model points to the tunnel data. The transformed model points at every 5 cm of the length of the tunnel were considered as current shape of the tunnel.

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Mobile surveying system for road assets monitoring and management

Cited by 2 publications

References 8 publications

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

Development and Testing of Octree-Based Intra-Voxel Statistical Inference to Enable Real-Time Geotechnical Monitoring of Large-Scale Underground Spaces with Mobile Laser Scanning Data

Tunnel Modeling Using Mobile Mapping Lidar Points

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