In this study,a localisation system without cumulative errors is proposed.First,depth odometry is achieved only by using the depth information from the depth camera.Then the point cloud cross-source map registration i...In this study,a localisation system without cumulative errors is proposed.First,depth odometry is achieved only by using the depth information from the depth camera.Then the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map.Furthermore,we fuse the odometry results with the point cloud to map registration results,so the system can operate effectively even if the map is incomplete.The effectiveness of the system for long-term localisation,localisation in the incomplete map,and localisation in low light through multiple experiments on the self-recorded dataset is demonstrated.Compared with other methods,the results are better than theirs and achieve high indoor localisation accuracy.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62088101)in part by STI 2030-Major Projects 2021ZD0201403+1 种基金the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2022B04)the Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ22F030022.
文摘In this study,a localisation system without cumulative errors is proposed.First,depth odometry is achieved only by using the depth information from the depth camera.Then the point cloud cross-source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map.Furthermore,we fuse the odometry results with the point cloud to map registration results,so the system can operate effectively even if the map is incomplete.The effectiveness of the system for long-term localisation,localisation in the incomplete map,and localisation in low light through multiple experiments on the self-recorded dataset is demonstrated.Compared with other methods,the results are better than theirs and achieve high indoor localisation accuracy.
