LTA-OM: Long-Term Association LiDAR-IMU Odometry and Mapping

Simultaneous localization and mapping (SLAM) technology is ubiquitously employed in ground robots, unmanned aerial vehicles, and autonomous cars. This paper presents LTA-OM: an efficient, robust, and accurate LiDAR SLAM system. Employing FAST-LIO2 and Stable Triangle Descriptor as LiDAR-IMU odometry and the loop detection method, respectively, LTA-OM is implemented to be functionally complete, including loop detection and correction, false positive loop closure rejection, long-term association mapping, and multi-session localization and mapping. One novelty of this paper is the real-time long-term association (LTA) mapping, which exploits the direct scan-to-map registration of FAST-LIO2 and employs the corrected history map to constrain the mapping process globally. LTA leads to more globally consistent map construction and drift-less odometry at revisit places. We exhaustively benchmark LTA-OM and other state-of-the-art LiDAR systems with 18 data sequences. The results show that LTA-OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA-OM is validated in a challenging scene - a multi-level building having similar structures at different levels. Besides, a multi-session mode is designed to allow the user to store current session’s results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life-long mapping. Furthermore, LTA-OM has valuable features for robot control and path planning, including high-frequency and real-time odometry, drift-less odometry at revisit places, and fast loop closing convergence. Moreover, LTA-OM is versatile as it is applicable to both multi-line spinning and solid-state LiDARs, mobile robots and handheld platforms.