The Identification and Compensation of Static Drift Induced by External Disturbances for LiDAR SLAM

Abstract

With the extending use of LiDAR SLAM in various areas, the interference of external disturbances on SLAM is becoming more and more obvious. Huge efforts have been made to reduce the drift error of LiDAR SLAM using graph-based methods. However, the mapping results can be severely affected by external disturbances under extreme conditions, which will limit the performance of graphbased methods. This study proposes a new strategy to reduce the static drift on a local scale by identifying and compensating the influence of external disturbances based on the localization results of LiDAR SLAM. Contrast experiments were first designed and performed to analyze the potential inducing factors of static drift, such as environment and vibration. The Kalman filter was adopted to estimate the speed and acceleration parameters based on the localization results of LiDAR SLAM. Then, an estimation criterion of static drift was established according to the interference of external disturbances on speed and acceleration. Finally, a static drift compensation method for LiDAR SLAM was proposed to compensate the drift of the pose. In the verification experiment, for 1866 data points, the identification accuracy of static drift was 97.32%, and the final positioning error of LiDAR SLAM was reduced from 4.9464 m to 0.1741 m after the compensation of static drift.

DyStSLAM: an efficient stereo vision SLAM system in dynamic environment

“…Lidar is a commonly used sensor for active sensor based SLAM. Lidar SLAM is reliable in theory [4][5][6]. The * Author to whom any correspondence should be addressed.…”