基于实例分割与光流的动态环境 SLAM

A visual semantic SLAM algorithm based on instance segmentation and optical flow is proposed to address the issue of excessive removal of features by traditional semantic SLAM algorithms in dynamic environments. The proposed algorithm utilizes a Mask R-CNN network to perform the instance-level segmentation of potential dynamic objects in an image, and also identifies and eliminates dynamic objects in the optical flow thread. The remaining static optical flow points and static feature points are then used to optimize the location estimation process, ensuring the optimal utilization of both semantic and optical flow information. The proposed algorithm is validated through testing on open datasets and an unmanned ground platform experiment. The experimental results indicate that the average error of the proposed algorithm is 75% and 8.5% lower than those of ORB-SLAM2 and Dyna-SLAM, respectively, on TUM dataset.