视觉同时定位与地图构建(Simultaneous localization and mapping,SLAM)过程中,动态物体引入的干扰信息会严重影响定位精度.通过剔除动态对象,修复空洞区域解决动态场景下的SLAM问题.采用Mask-RCNN获取语义信息,结合对极几何方法对动态对象进行剔除.使用关键帧像素加权映射的方式对RGB和深度图空洞区域进行逐像素恢复.依据深度图相邻像素相关性使用区域生长算法完善深度信息.在TUM数据集上的实验结果表明,位姿估计精度较ORB-SLAM2 平均提高85.26%,较DynaSLAM提高28.54%,在实际场景中进行测试依旧表现良好.
Dynamic SLAM Based on Background Restoration
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.
simultaneous localization and mapping(SLAM)semantic segmentationepipolar geometryRGB repairdepth repair
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