A Tightly Coupled LiDAR-Inertial-Visual SLAM With Enhanced Dual Subsystems for Limited FoV LiDAR

Simultaneous localization and mapping (SLAM) is a fundamental problem for mobile robots. In recent years, solid-state LiDAR have garnered growing attention owing to their enhanced reliability, cost-effectiveness and lightweight. However, in comparison to traditional rotating LiDAR, employing solid-state LiDAR for SLAM is challenging due to their small field of view (FoV) in the lateral direction. In unstructured environments that lack distinct features and geometric structures, small FoV introduces error in pose estimation, particularly in ill-conditioned directions, leading to LiDAR degeneration. Additionally, the small FoV leads to a higher proportion of dynamic points in the LiDAR frame, which also contributes to error in point cloud registration. To address these challenges, we propose a LiDAR-inertial-visual SLAM method for solid-state LiDAR, which overcomes limitations of small FoV. This method consists of a LiDAR-inertial odometry to provide geometric constraint and a visual-inertial odometry to provide visual constraint. To resist the LiDAR degeneration, a novel residual construction method is proposed, which enhances the dimensionality of residual in visual-inertial odometry. Furthermore, we propose a keyframe-based static point registration method to effectively decrease incorrect constraints from dynamic points in LiDAR-inertial odometry. Various comparative experiments demonstrate the performance and advantages of the proposed LiDAR-inertial-visual SLAM method in terms of robustness and accuracy.