Mobile Robot SLAM Based on Multi-Sensor Fusion

With the development of autonomous driving and artificial intelligence tech-nology, multi-sensor fusion SLAM has become key to achieving high-precision autonomous navigation. By integrating data from different sensors, it significantly enhances the environment sensing and positioning accuracy of SLAM systems. This paper introduces the classification and fusion archi-tecture of multi-sensor fusion SLAM, verifies its effectiveness through experimental comparisons of map construction before and after fusion, and explores particle filter and graph optimization-based fusion methods, sum-marizing their principles, key formulas, and algorithms. Finally, the paper discusses current limitations and proposes future research directions, providing a theoretical reference for the development of SLAM technology.