Simultaneous localization and dense scene reconstruction approach based on stereo VIO
Aiming at the problems of pose drift and poor mapping quality of traditional dense simultaneous localization and mapping(SLAM)in highly dynamic scenes,a simultaneous localization and dense scene reconstruction approach based on stereo visual-inertial odometry(VIO)is proposed.Firstly,to tackle the issue of pose drift,the bias information equation in the process of extracting 3D landmark points with a stereo camera is deduced,and the observations of inertial measurement unit are used to filter landmark points to improve the positioning accuracy and robustness of the system.Then,to improve the mapping quality,an incremental map construction scheme is designed that combines the CREStereo multi-view disparity estimation network with truncated signed distance function(TSDF),which eliminates erroneous depth estimation in the keyframe sequence based on the effective visual range of the stereo model and employs voxel Hashing to dynamically build a globally consistent 3D scene.Finally,the real-time positioning and 3D reconstruction performance of the proposed algorithm are comparatively tested on the EuRoC dataset.The experimental results show that the positioning accuracy of the proposed algorithm is improved by 9.20% compared with VINS-Mono in high dynamic scenes,and can achieve high quality map construction under high dynamic and weak texture conditions.
万方数据
维普
