In this study,to provide a detailed three-dimensional(3D)semantic map for mobile robots and support precise positioning,a semantic simultaneous localization and mapping(SLAM)method of a robot is put forward based on RGB-Depth(RGB-D)information and deep learning results.First,the ORB-SLAM2 algorithm framework is improved,and a visual SLAM system is presented to build the dense point cloud map.Afterward,the deep learning target detection algorithm YOLO v5 is merged with a visual SLAM system,which inversely maps 3D point cloud semantic labels.The data association and object model update are completed in combination with point cloud segmentation.The map information is stored in the form of an octree map.A 3D semantic SLAM experiment is con-ducted based on the mobile robot platform in the lab environment.The experimental results confirm the effective-ness of semantic information mapping,point cloud segmentation with semantic matching,and 3D semantic map construction of the proposed semantic SLAM algorithm.
关键词
移动机器人/深度学习/视觉同步定位与建图/目标识别/点云分割/数据关联/八叉树/语义地图
Key words
mobile robot/deep learning/visual simultaneous localization and mapping(SLAM)/object recogni-tion/point cloud segmentation/data association/octree/semantic map
维普
万方数据