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Automatic and accurate mapping and modeling of underground infrastructure has become indispensable for several important tasks ranging from urban planning and construction to safety and hazard mitigation. However, this offers several technical and operational challenges. The aim of this work is to develop a portable automated mapping solution for the 3D mapping and modeling of underground pipe networks during renovation and installation work when the infrastructure is being laid down in open trenches. The system is used to scan the trench and then the 3D scans obtained from the system are registered together to form a 3D point cloud of the trench containing the pipe network using a modified global ICP (iterative closest point) method. In the 3D point cloud, pipe-like structures are segmented using fuzzy C-means clustering and then modeled using a nested MSAC (M-estimator SAmpling Consensus) algorithm. The proposed method is evaluated on real data pertaining to three different sites, containing several different types of pipes. We report an overall registration error of less than 7 % , an overall segmentation accuracy of 85 % and an overall modeling error of less than 5 % . The evaluated results not only demonstrate the efficacy but also the suitability of the proposed solution.

International audience

International audience

In this paper we present an automatic obstacle detection and tracking system for efficient inter-distance and anti-collision management that fuses both 3D LiDAR and 2D image data. The obstacles are first detected both in LiDAR scans and camera images and the data are then fused together. Even though LiDAR based detections are very accurate they are slower than image based detections. Hence, the proposed method helps in obtaining the state estimates more quickly with good accuracy. The unique fusion technique presented uses the detected object's geometrical information to extract the depth information at each image scan which is then corrected at each LiDAR scan. The results evaluated on real data demonstrate the prowess as well as the applicability of the proposed method which can be used for different vehicle safety applications.