Abstract: SLAM in complex environments is one of the challenging tasks in the field of robot autonomous navigation research. The drastic changes in the surrounding spatial environment can lead to drift and overlap in SLAM mapping, thereby reducing mapping accuracy. To address this issue, this paper proposes an effective adaptive optimization method for point cloud thresholding, improving the applicability of SLAM algorithms in complex environments. The algorithm calculates the depth information of the point cloud in real-time and adaptively optimizes the effective point cloud threshold based on the fluctuation of depth information and the coefficient of variation of point cloud distribution, thereby achieving closed-loop control. Experiments show that the proposed threshold adaptive optimization method significantly improves the mapping performance of fast and direct LiDAR with inertial odometry algorithms in complex environments. It corrects the odometer coordinate errors of this algorithm in narrow environments and reduces loop closure positioning errors by 7.5%.

Key words: simultaneous localization and mapping, depth information, dispersion coefficients, adaptive thresholds, point clouds