Abstract: Aiming at the problems that dynamic objects (such as pedestrins, vehicles, animals) appear in visual SLAM(Simultaneous Localization and Mapping) in real scenes, affect the accuracy of algorithm positioning and mapping, the YOLOv3-ORB-SLAM3(Oriented FAST and Rotated BRIEF-Simultaneous Localization and Mapping 3) algorithm is proposed based on ORB-SLAM3. The algorithm adds a semantic thread on the basis of ORB- SLAM3, and the thread uses YOLOv3 to perform semantic recognition target detection on dynamic objects in the scene. The outliers are removed from the extracted feature points on the tracking thread, and the static environment area extracted by the ORB feature, thereby the positioning accuracy of the visual SLAM algorithm is improved. The TUM(Technical University of Munich) data set is used to verify the positioning accuracy of the algorithm in monocular and RGB-D(Red, Green and Blue-Depth) modes. The verification results show that the dynamic sequence of the YOLOv3-ORB-SLAM3 algorithm in monocular mode is about 30% lower than that of the ORB-SLAM3 algorithm in RGB-D mode, the dynamic sequence decreases by 10% , and the static sequence does not decrease significantly.

Key words: you only look once v3(YOLOv3), oriented FAST and rotated BRIEF-simultaneous localization and mapping 3(ORB-SLAM3), dynamic scene, monocular camera, red, green and blue-depth(RGB-D)