Visual Simultaneous Localization and Mapping Algorithm Based on Dynamic Target Detection

This study proposes a visual simultaneous localization and mapping (SLAM) algorithm based on dynamic target identification to address the problem of low positioning accuracy of conventional visual SLAM algorithm in dynamic scenes. First, the input image frame is preprocessed in front of the visual SLAM system, and the potential dynamic area of the image is deleted by the target detection network you look only once, v3 (YOLO, v3). Furthermore, the input image optimizes the homography matrix using reprojection error to obtain the motion compensation frame and four-frame difference image. Then, the four-frame difference image is filtered, binarized, and morphologically processed. Finally, combined with YOLO v3 network to optimize the dynamic target detection results, reduce noise generated by strong parallax and image blur. The feature points of the static area are used for visual SLAM tracking, mapping, and loop detection. The experimental results regarding common TUM data sets indicate that the algorithm can effectively improve the accuracy of visual SLAM in a dynamic environment.