Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 289-295, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/289/2014/
doi:10.5194/isprsarchives-XL-3-289-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
Efficient point cloud collision detection and analysis in a tunnel environment using kinematic laser scanning and K-D Tree search
J. Schauer1 and A. Nüchter2 1School of Engineering and Science, Automation Group, Jacobs University of Bremen gGmbH, Campus Ring 1, Bremen 28759, Germany
2Informatics VII : Robotics and Telematics, Julius-Maximilians-University Würzburg, Am Hubland, Wüzburg 97074, Germany
Keywords: Collision Detection, K-D Tree, Kinematic Laser Scanning, 3D Point Clouds Abstract. Measuring the structure gauge of tunnels and other narrow passages has so far been the only way to evaluate whether large vehicles can pass through them. But especially for very long vehicles like train wagons and their cargo, the structure gauge is an insufficient measure because the center part of the vehicle between two bogies will inevitably leave the swept volume of its cross section when moving along any other trajectory than a straight line perpendicular to its cross section. In addition, the vehicle as well as the cargo must keep a minimum safety margin from the environment at all points of its trajectory. This paper explores an automated method to check for possible collisions of a model represented by a 3D point cloud moving through the 3D point cloud of an environment. We were given environment data of a train track through a narrow tunnel where simply relying on the structure gauge would indicate that a given wagon would pass through without any collision even though in reality, the train wagon would collide with the inner tunnel wall inside a sharp turn of the tracks. The k-d tree based collision detection method presented in this paper is able to correctly highlight these collisions and indicate the penetration depth of each colliding point of the environment into the model of the train wagon. It can be generalized for any setup where two static point clouds have to be tested for intersection along a trajectory.
Conference paper (PDF, 978 KB)


Citation: Schauer, J. and Nüchter, A.: Efficient point cloud collision detection and analysis in a tunnel environment using kinematic laser scanning and K-D Tree search, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 289-295, doi:10.5194/isprsarchives-XL-3-289-2014, 2014.

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