Unsupervised Motion Removal for Dynamic SLAM  

作　　者：CHEN Hao ZHANG Kaijiong CHEN Jun ZHANG Ziwen JIA Xia 

机构地区：[1]ZTE Corporation,Shenzhen 518057,China [2]State Key Laboratory of Mobile Network and Mobile Multimedia Technology,Shenzhen 518055,China

出　　处：《ZTE Communications》2024年第4期67-77,共11页中兴通讯技术（英文版）

摘　　要：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.

关 键 词：dynamic RGBD SLAM update module motion estimation scene flow 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]