Analysis on Simultaneous Localization and Mapping of Mobile Robot Based on Two Multi-sensor Fusion Algorithm

This paper studies the problem of simultaneous localization and autonomous mapping of mobile machines, based on multiple sensor fusion SLAM algorithms. This method can solve the problem that the feature information of the collected image is not accurate or lost when a single laser or vision sensor is used in simultaneous localization and mapping (SLAM) research of mobile robots, which leads to localization failure. First, combining LIO-SAM algorithm and LVI-SAM algorithm, the simulation and construction of large scene maze environment are carried out for the mobile robot equipped with Lidar and depth camera. Then through the experimental simulation, the map built by the two algorithms is compared and analyzed. The results show that the map built by LID-SAM algorithm is more accurate in the maze environment with fewer feature points, and the map building effect is obviously better than that of LVI-SAM algorithm.