Research on semantic SLAM algorithm based on deep learning

In practical applications, mobile robots or UAVs often need to navigate and locate in a dynamic environment, but traditional SLAM algorithms often perform poorly in the face of dynamic environments. Therefore, dynamic SLAM has become one of the research hotspots. In order to solve the problems of low positioning accuracy and poor robustness of traditional visual SLAM in dynamic scenes. In this paper, an improved algorithm based on ORBSLAM3 is proposed.Under the condition of keeping the original framework unchanged, the algorithm adds a new semantic thread, combines Mask R-CNN to segment the image frame, extracts the keyframes for optimization, removes the feature points of the dynamic object, and retains the feature points of the original static object. Finally, comparative experiments are carried out based on the TUM dataset, and the final results show that the proposed algorithm is superior to the existing algorithms, and the positioning accuracy and robustness are improved.