We propose a dynamic simultaneous localization and mapping technology for unsupervised motion removal (UMR-SLAM),which is a deep learning-based dynamic RGBD SLAM. It is the first time that a scheme combining scene flow and deep learning SLAM is proposed to improve the accuracy of SLAM in dynamic scenes,in response to the situation where dynamic objects cause pose changes. The entire process does not require explicit object segmentation as supervisory information. We also propose a loop detection scheme that combines optical flow and feature similarity in the backend optimization section of the SLAM system to improve the accuracy of loop detection. UMR-SLAM is rewritten based on the DROID-SLAM code architecture. Through experiments on different datasets,it has been proven that our scheme has higher pose accuracy in dynamic scenarios compared with the current advanced SLAM algorithm.
万方数据
维普
