DALI-SLAM: Degeneracy-aware LiDAR-inertial SLAM with novel distortion correction and accurate multi-constraint pose graph optimization

LiDAR-Inertial simultaneous localization and mapping (LI-SLAM) plays a crucial role in various applications such as robot localization and low-cost 3D mapping. However, factors including inaccurate motion distortion estimation and pose graph constraints, and frequent LiDAR feature degeneracy present significant challenges for existing LI-SLAM methods. To address these issues, we propose DALI-SLAM, an accurate and robust LI-SLAM that consists of degeneracy-aware LiDAR-inertial odometry (DA-LIO) with a dual spline-based motion distortion correction (DS-MDC) module, and multi-constraint pose graph optimization (MC-PGO). Considering the cumulative errors of micro-electromechanical systems (MEMS) inertial measurement unit (IMU) integration, two continuous-time trajectories in the sliding window are fitted to update the discrete IMU poses for accurate motion distortion correction. In the LiDAR-inertial fusion stage, LiDAR feature degeneracy is detected by analyzing the Jacobian matrix and a remapping strategy is introduced into the updating of error state Kalman Filter (ESKF) to mitigate the influence of degeneracy. Furthermore, in the back-end optimization stage, three types of submap constraints are accurately built with dedicated strategy through a robust variant of the iterative closest point (ICP) method. The proposed method is comprehensively validated using data collected from a helmet-based laser scanning system (HLS) in representative indoor and outdoor environments. Experiment results demonstrate that the proposed method outperforms the SOTA methods on the test data. Specifically, the proposed DS-MDC module reduces trajectory root mean square errors (RMSEs) by 7.9 %, 5.8 %, and 3.1 %, while the degeneracy-aware update strategy achieves additional reductions of 43.3 %, 17.7%, and 4.9 %, respectively, across three typical sequences compared to existing methods, thereby effectively improving trajectory accuracy. Furthermore, the results of DA-LIO demonstrate a maximum RMSE within 1 kilometer of approximately 1 meter in outdoor environments, achieving superior performance compared to the SOTA method FAST-LIO2. After performing MC-PGO, the RMSEs of the trajectories are reduced by 25.2 %, 9.2 %, and 52.4 %, respectively, across three typical sequences, demonstrating better performance compared to the SOTA method HBA. Code will be available at https://github.com/DCSI2022/DALI_SLAM.