数据采集与处理2024，Vol.39Issue(5)：1204-1213,10.DOI:10.16337/j.1004-9037.2024.05.012
基于背景修复的动态SLAM
Dynamic SLAM Based on Background Restoration
摘要
Abstract
In the context of simultaneous localization and mapping(SLAM),the accuracy of positioning is significantly affected by interference caused by dynamic objects.This paper addresses the challenges of SLAM in dynamic environments through the removal of dynamic objects and restoration of empty regions.Semantic information is obtained using Mask-RCNN,while a polar geometry approach is employed to eliminate dynamic objects.Keyframe pixel weighted mapping enables precise recovery of void regions in both RGB and depth maps at a pixel-by-pixel level.Experimental results on the TUM dataset demonstrate an average improvement of 85.26%in pose estimation accuracy compared to ORB-SLAM2,as well as a 28.54%enhancement over DynaSLAM performance.The proposed method exhibits robust performance even in real-world scenarios.关键词
同时定位与地图构建/语义分割/对极几何/RGB修复/深度修复Key words
simultaneous localization and mapping(SLAM)/semantic segmentation/epipolar geometry/RGB repair/depth repair分类
信息技术与安全科学引用本文复制引用
李嘉辉,范馨月,张干,张阔..基于背景修复的动态SLAM[J].数据采集与处理,2024,39(5):1204-1213,10.基金项目
国家自然科学基金(62271096). （62271096）
