Improvement of 3D-SLAM Accuracy by Removing Moving Objects on 3D-LiDAR Point Cloud Using Image Recognition in Web Camera

In recent years, robots have been developed that can move autonomously in human environments such as restaurants and airports. For such autonomous mobility, it is important to create a map in advance, and a typical example is Simultaneous Localization and Mapping (SLAM). We created a 3D perception filter that is capable of detecting and eliminating moving point clusters from the input point cloud taken in an indoor environment. In this study, we propose a system that detects moving objects based on camera image recognition and uses the results to construct a more accurate map by minimizing the influence of pedestrians.