A 3D LiDAR-Inertial Tightly-Coupled SLAM for Mobile Robots on Indoor Environment

摘要

Indoor environments are considered challenging due to their perceived degradation. This is attributed to both the homogeneity of indoor scene structures and the scarcity of feature points, particularly in long corridor environments. Additionally, low-resolution 3D laser scanners may not effectively capture the real information of the indoor environment. To address these issues, this paper proposes a tightly coupled SLAM method based on factor graph optimization for laser-inertial fusion. Firstly, a dynamic feature point extraction method is designed to adaptively adjust the feature extraction quantity by detecting indoor degradation, constructing rich and robust feature constraints to enhance pose estimation accuracy. Next, robust and precise SLAM in indoor environments is achieved through factor graph optimization. Finally, validation is conducted on the KITTI dataset and in real scenarios. Experimental results demonstrate that the proposed method exhibits superior accuracy, real-time performance, and robustness compared to ALOAM, LeGO-LOAM, FLOAM, and LIO-SAM in terms of localization, mapping precision, and global consistency. The method effectively reduces cumulative errors and ensures the global consistency of map construction.