3D Semantic Map Construction Using Improved ORB-SLAM2 for Mobile Robot in Edge Computing Environment

Although the existing localization and mapping (SLAM) technology of indoor mobile robot has made great development, its intelligence and environmental perception ability still cannot meet the needs of service and inspection. Therefore, based on edge computing environment, a 3D semantic map construction of mobile robot based on improved ORB-SALM2 is proposed. Firstly, the improved yolov3 algorithm is used to detect indoor objects, and then the real-time semantic segmentation network model based on deep learning is used to segment indoor objects to achieve the classification of pixel points of objects on two-dimensional images, and BAFF feature fusion algorithm is introduced to improve the accuracy of semantic segmentation model. Then, through the SLAM system, we estimate the pose of the image in the result of semantic segmentation, and use the depth information to project it into the three-dimensional environment to build the three-dimensional semantic map. Finally, the experiment platform of mobile robot is built to verify the stability of ORB-D and thermal imaging sensor registration technology, the accuracy and real-time of building three-dimensional environment thermal field map, and the accuracy of robot positioning using thermal infrared and depth image.