Ground Robot Relocation Method Based on UAV Point Cloud Map
In response to the challenge of relocalization in air-ground collaborative systems without the support of Global Navigation Satellite System(GNSS),and the associated issues of insufficient accuracy,a coarse-to-fine relocalization algorithm based on a three-dimensional point cloud map is proposed.The algorithm eliminates the influence of invalid point clouds from the sky and ground through index filtering,performs coarse localization by extracting global features from the point cloud and applying truncated least squares estimation,and then employs voxel-based iterative closest point(ICP)for precise optimization to obtain the more accurate localization results.A ground robot localization and autonomous navigation framework is constructed based on an aerial global map and the feasibility of the framework is validated through experiments on a simulation platform,while the real-time and accuracy of the ground robot relocalization algorithm is verified.
ground robotrelocalizationUAVglobal point cloud mapautonomous navigation
NETL
NSTL
万方数据
