A Stereo Vision Based Obstacle Detection System for Agricultural Applications

In this paper, an obstacle detection system for field applications is presented which relies on the output of a stereo vision camera. In a first step, it splits the point cloud into cells which are analyzed in parallel. Here, features like density and distribution of the points and the normal of a fitted plane are taken into account. Finally, a neighborhood analysis clusters the obstacles and identifies additional ones based on the terrain slope. Furthermore, additional properties can be easily derived from the grid structure like a terrain traversability estimation or a dominant ground plane. The experimental validation has been done on a modified tractor on the field, with a test vehicle on the campus and within the forest.