越野环境下无人履带平台的道路可通行性分析

To deal with the problems of fuzzy road features and complex terrain in off-road environment, a road traversability analysis method for unmanned tracked platforms is proposed based on the fusion scheme of camera and lidar for sensing. Firstly, the semantic point cloud is obtained based on image semantic segmentation, and the passable area is roughly extracted. Then the three-dimensional point cloud is used to describe the ground geometric features, and road traversability is analyzed considering the trafficability constraints of the tracked platform. Finally, a three-dimensional traversable grid map containing road surface attributes and ground geometry information is generated. The definition of platform traversability in the proposed method reflects the strong coupling relationship between the platform and the environment. The experimental results show that the algorithm can stably establish the traversable map, provide good guidance for platform planning and control, and is conducive to the stable trafficability of the unmanned tracked platform in the complex off-road environment.