Robust LiDAR-Inertial Localization and Mapping With Novel Dynamic Object Removal in Dynamic Environments

Abstract

LiDAR localization and mapping play an important role in mobile robotics and autonomous driving systems, but points from dynamic objects in urban environments can be incorrectly associated with the local map during scan-to-map registration, introducing erroneous constraints into pose estimation and thereby degrading localization accuracy. Existing occupancy-based dynamic object removal (DOR) methods typically face two challenges: insensitivity to overlapped dynamic regions and high sensitivity to occlusions. To address these issues, we propose a LiDAR-inertial localization framework with plug-and-play DOR. The DOR module removes dynamic points before registration, thereby reducing such erroneous constraints while preserving reliable static structures for more stable scan-to-map associations. It combines occupancy difference detection, connectivity-based region completion, and spatiotemporal overlap discrepancy to improve the completeness of dynamic region detection in overlapped regions and reduce the false removal of static points caused by occlusions. In addition, a multilayer consistency check based on Kullback–Leibler (KL) divergence is introduced during scan matching to reject surface constraints with inconsistent distributions, thereby improving data association reliability. The proposed method is evaluated on the KITTI, ECMD, UrbanNav, UrbanLoco, and a self-collected campus dataset. Compared with representative SOTA baselines, including VoxelMap, PV-LIO, TRLO, Dynamic-LIO, and BTSA, our method achieves the lowest average absolute trajectory error (ATE) among the compared methods, with 1.91 m on KITTI and 1.41 m on the selected ECMD comparison subset, demonstrating improved localization accuracy across environments with different scales and dynamic levels.