LiDAR SLAM Global Positioning Uncertainty Estimation Based on Lie Group and MHSS theory

Abstract. LiDAR based simultaneous localization and mapping (SLAM) plays an important role for real-time localization and 3D mobile mapping of autonomous systems. However, the long-term scan-to-scan matching in the SLAM can introduce uncertainty into the position estimation. which results in a large drift. In this paper, we specifically focus on real-time estimation of the global positioning uncertainty of LiDAR SLAM so that it can enable the graceful weighting of LiDAR SLAM with other positioning systems in multi-sensor fusion localization. We introduce Lie group theory and multiple fault hypothesis solution separation (MHSS) method into a Kalman-filter based LiDAR SLAM framework. First, the scan-to-scan matching uncertainty is obtained by establishing fault hypothesis utilizing MHSS method. Then the global positioning uncertainty is propagated on Lie group based on the scan-to-scan matching uncertainty in terms of the relative position and rotation. The NCLT dataset is used to validate the proposed method. Experimental results show that: comparing with previous solutions that treat scan-to-scan matching uncertainty as a constant, the proposed method is more adaptive and robust. And the real-time global positioning uncertainty estimation can envelop the real SLAM absolute trajectory error (ATE) for the most of the time and can reflect the real changing tendency of ATE.