Research on dynamic robust visual SLAM method based on deep learning

Visual simultaneous localisation and mapping is a fundamental technology in autonomous mobile robotic systems. The presence of dynamic objects in the environment can lead to incorrect feature matching, and factors such as fluctuating external lighting conditions can introduce instability to the system, thus limiting the practical application of VSLAM. In this paper, a robust VSLAM system for dynamic environments is proposed. Based on ORB-SLAM2, we use YOLO5 to enhance the consistency of the front-end combined with optical flow motion detection to detect dynamic targets in the environment and reject their feature points. Superpoint replaces ORB in the feature extraction process, which further enhances the adaptability of the system to the instability of the external environment. From the experimental results, it can be seen that the improved VSLAM, the real motion trajectory is extremely close to the estimated trajectory, and the error is greatly reduced.