Abstract
Simultaneous Localization and Mapping (SLAM) serves as a foundational technology for autonomous systems operating within large-scale, complex environments. Traditional SLAM methodologies, however, are prone to altitude-axis distortions resulting from cumulative errors. To mitigate these issues, Gravity-Constrained SLAM (GC-SLAM) is introduced as a novel computational method that integrates gravity constraints and incremental optimisation to enhance mapping accuracy and computational efficiency. GC-SLAM incorporates a gravity constraint handling actor within the global optimisation algorithm, effectively reducing vertical-axis errors caused by accumulated drift during mapping. Furthermore, an incremental optimisation strategy is employed to manage the computational complexity associated with increasing map size. Performance evaluations of GC-SLAM are conducted on the KITTI dataset and large-scale environments, comparing its effectiveness against state-of-the-art SLAM-based algorithms, including FAST-LIO2, LIO-SAM (Lidar Inertial Odometry and SLAM), Lego-LOAM (Lightweight and Ground-optimised Lidar Odometry and Mapping), and A-LOAM (Advanced Lidar Odometry and Mapping). Experimental results demonstrate that GC-SLAM effectively suppresses vertical-axis distortions, significantly enhances localisation accuracy, and outperforms competing methods.