Abstract: The traditional visual SLAM algorithm is based on the static environment assumption when recovering scene information and camera motion. The dynamic objects in the scene will reduce the robustness of the algorithm and the final positioning accuracy. In this paper, we propose to combine the image semantic segmentation based on deep learning method with the traditional visual SLAM to reduce the interference of dynamic objects on the positioning results. Firstly, a supervised convolutional neural network (CNN) is used to segment the dynamic objects in the input image to obtain the semantic image. Secondly, after extracting feature points from the original image, the feature points of dynamic objects are eliminated object feature points according to the semantic image, and the feature points of static objects are saved. Finally, the monocular SLAM method with points is used to track the camera motion based on the saved feature points. Experiments on the ApolloScape datasets show that compared with the traditional method, the proposed method improves the positioning accuracy in dynamic scenes by about 17%.

Key words: monocular visual SLAM, dynamic objects, CNN, semantic segmentation, deep learning method