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Measuring the structure gauge of tunnels and other narrow passages has so far been the only way to evaluate whether large vehiclescan pass through them. But especially for very long vehicles like train wagons and their cargo, the structure gauge is an insufficientmeasure because the center part of the vehicle between two bogies will inevitably leave the swept volume of its cross section whenmoving along any other trajectory than a straight line perpendicular to its cross section. In addition, the vehicle as well as the cargomust keep a minimum safety margin from the environment at all points of its trajectory. This paper explores an automated method tocheck for possible collisions of a model represented by a 3D point cloud moving through the 3D point cloud of an environment. Wewere given environment data of a train track through a narrow tunnel where simply relying on the structure gauge would indicate that agiven wagon would pass through without any collision even though in reality, the train wagon would collide with the inner tunnel wallinside a sharp turn of the tracks. The k-d tree based collision detection method presented in this paper is able to correctly highlightthese collisions and indicate the penetration depth of each colliding point of the environment into the model of the train wagon. It canbe generalized for any setup where two static point clouds have to be tested for intersection along a trajectory

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Efficient point cloud collision detection and analysis in a tunnel environment using kinematic laser scanning and K-D Tree search