A Novel Multi-Sensor Fusion SLAM Approach for Mobile Robot

This paper proposes a novel multi-sensor fusion SLAM approach for mobile robots, which integrates data from an IMU, a 2D LiDAR, and an RGB-D camera within a unified fusion architecture. First, to address the challenges of inaccurate and inefficient visual point cloud acquisition, enhanced neural networks are employed to extract global–local visual feature point clouds. These are then matched with LiDAR point clouds to improve spatial consistency. Second, to overcome the limitations of conventional fuzzy adaptive unscented Kalman filters (UKF) — namely low positioning accuracy and poor adaptability — an improved fuzzy adaptive UKF algorithm is introduced, which fuses IMU data and point cloud matching results. The optimal robot pose and environmental map are subsequently obtained through joint optimization. The proposed approach is evaluated through both simulation and real-world experiments. Results demonstrate that the method achieves accurate pose estimation, precise localization and mapping performance, and strong robustness in complex environments. The proposed approach significantly outperforms existing LiDAR-Visual-IMU SLAM approaches.