RESEARCH ON LIDAR SLAM METHOD WITH FUSED POINT CLOUD INTENSITY INFORMATION

Abstract

Under the current trend of intelligence and automation, simultaneous positioning and mapping technology has become one of the research hotspots. The main problems of SLAM technology research are to improve the robustness of mapping and positioning, establish an efficient back-end optimization system, and improve the generalization of SLAM technology. This paper proposes to fuse the intensity information of the point cloud and the geometric information of the environment scene to construct a globally consistent environment feature descriptor and use the non-iterative two-step method to perform the nearest neighbour search on the point cloud in the point cloud registration stage. Then use the globally consistent descriptor that has been constructed to extract the laser point cloud descriptor by using the ring partition method, combine the method based on domain search to search for the closest point cloud frame, and finally use Intensity-ICP to complete the loopback frame. Fine registration, outputs the optimal pose transformation, to complete the loop detection. We use our self-built mobile platform to verify the robustness and generalization of the improved laser SLAM algorithm in public datasets and campus datasets. Experimental results show that the improved algorithm reduces the trajectory drift of the mobile platform and improves the efficiency of point cloud registration and loop closure detection.