A Multi-Sensor Fusion SLAM Method with Ground Constraints for Inspection Robots

Abstract

This study proposes a multi-sensor fusion-based Simultaneous Localization and Mapping framework (LIOG-SLAM) to address positioning errors and drift issues encountered by substation inspection robots in large-scale substations. By integrating 3D LiDAR, Inertial Measurement Unit (IMU), Wheel Odometry (ODO), and Global Navigation Satellite System (GNSS) data, combined with ground-optimized odometry constraints, IMU-ODO joint pre-integration, sliding window marginalization, and loop closure detection, positioning accuracy and system robustness have been enhanced. In the KITTI dataset and Gazebo simulation environment, LIOG-SLAM demonstrated higher accuracy and lower error compared to traditional algorithms such as A-LOAM and LIO-SAM. Specifically, the root mean square error of APE decreased by 9.1 m and 5.207 m on the same sequence dataset, while maintaining efficient real-time performance in complex environments. This method offers a novel solution for efficient inspection in substations.