SMF-SLAM: Sliding mode filtering based optimization algorithm for LiDAR SLAM

In recent years, with the rapid development of LiDAR technology, there is an increasing demand for 3D change detection in automatic driving, infrastructure monitoring, and so on. Aiming at the research of LIDAR SLAM point cloud building model, this paper proposes an improved LIDAR SLAM system that combines sliding mode filtering and adaptive curvature thresholding method to improve the detection accuracy and efficiency. The adaptive threshold function based on historical threshold and current curvature change is designed to enhance the edge feature extraction accuracy by dynamically adjusting the threshold. Meanwhile, sliding mode filtering is used for local pose estimation to effectively improve the efficiency and accuracy of the system. Finally, a large number of experiments are conducted on the robot platform to verify the effectiveness of the algorithm. The experimental results show that the method can effectively improve the number of feature points extracted and the accuracy of map building in outdoor environments.