VSLAM method based on object detection in dynamic environments - Liu, Jia; Gu, Qiyao; Chen, Dapeng; Yan, Dong (Frontiers in Neurorobotics, 2022)
Many fields now require improved SLAM systems to adapt to more complex and highly dynamic environments. The commonly used VSLAM algorithm has problems such as excessive pose estimation errors and easy loss of camera tracking in high dynamic scenes. To solve these problems, we propose a real-time tracking and mapping method based on GMM combined with YOLOv3. The method utilizes the ORB-SLAM2 system framework and improves its tracking thread. Our method combines the affine transformation matrix to correct the front and back frames, and employs GMM to model the background image and segment the foreground dynamic region. Then, the obtained dynamic region is sent to the YOLO detector to find the possible dynamic target. The method uses the improved Kalman filter algorithm to predict and track the detected dynamic objects in the tracking stage. Before building a map, the method filters the feature points detected in the current frame and eliminates dynamic feature points. Finally, we use the TUM dataset to verify the proposed method. The results show that the method proposed in this paper is more robust under the dynamic data set.