无人平台越野环境下同步定位与地图创建

Simultaneous localization and mapping (SLAM) plays a more and more important role in the environment perception system of unmanned ground vehicle (UGV). A hierarchical global topological map is proposed to solve the low real time capability problem during processing of 3D lidar point cloud map, which divides the entire map into many submaps consisting of large numbers of tree-structure-based voxels, and the submaps are organized with the help of topology. The probabilistic methods are used to represent the state of each voxel being occupied/null. A transitable area is extracted based on the submaps for the UGV's path planning. With the help of an inertial measurement unit (IMU) and two 3D lidars, a branch and bound search (BBS)-based loop detection algorithm called RHM-ICP algorithm is proposed to realize a real time 6-degrees-of-freedom global pose optimization with the help of Ceres, which is to process the sparse pose adjustment (SPA). Experimental results obtained from a real large-scale off-road environment shows an effective reduction of lidar odometry pose accumulative error with a global location error of less than 1 m and a good performance of 3D mapping.