Dynamic Environment Visual SLAM Based on Unmanned Delivery Experimental Platform in Closed Park Area
Our research proposes a visual SLAM system tailored to dynamic environments within enclosed park area, aiming to address the issues of low localization accuracy and severe map offset in SLAM algorithms. Based on an unmanned delivery experiment platform, this system utilizes ORB_SLAM2 as the framework, and supplemented with a behavior recognition thread based on human keypoint ex-traction. While extracting ORB feature points from input images, the system also employs HRNet network to extract human keypoints, conducts behavior recognition based on 30-frame image content to determine the movement status of human subjects in the images, and subsequently screens and eliminates dynamic feature points accordingly. Finally, pose estimation is carried out using static feature points. Experimental results demonstrate that, compared with ORB_SLAM2 and DS_SLAM, our research proposed algorithm achieves a balance between system accuracy and speed in dynamic subsequences of the TUM dataset, effectively enhancing the accuracy of pose estimation.
万方数据
维普
