LIDAR-BASED INITIAL GLOBAL LOCALIZATION USING IMPERFECT ARCHITECTURAL SKELETON INFORMATION

Initial global localization of a mobile robotic platform is the foundation for its navigation and mapping, especially when the platform enters into unknown environments. In GNSS-denied indoor scenes, LiDAR is widely used for robot localization, especially in indoor scenes with poor lighting. In most existing LiDAR-based initial global localization methods, it is necessary to build the point cloud reference map in advance, which costs a large quantity of manpower and time. For this reason, a LiDAR-based initial global localization method using imperfect architectural skeleton information is proposed in this work. Firstly, we propose a lightweight management scheme for collected imperfect architectural information, which is convenient for efficient registration with real scans. Secondly, we extract architectural skeletons (stable man-made structures such as walls and columns) from both architectural information and real scans, and design them as line pairs feature patterns like P-LP, V-LP and C-LP. Thirdly, we propose a matrix descriptor for line pairs feature patterns description and initial matching. Finally, we construct error equations to estimate the pose by initial matching line pairs, and acquire the optimal localization results with the highest hit ratio on architectural grid map. A mobile robotic platform with the 16 beam LiDAR is experimented in typical indoor scenes such as rooms, corridors and undergrounding parking lots. Experiments show that the success rate of initial global localization reaches 80%, the average position error is about 0.10m and the running time is about 400ms per 1000 scans, which meet the requirements of indoor autonomous driving.