Ground Robot Relocation Method Based on UAV Point Cloud Map

doi:10.16182/j.issn1004731x.joss.23-0751

Abstract

Abstract:

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.

Key words: ground robot, relocalization, UAV, global point cloud map, autonomous navigation

CLC Number:

TP242

Huang Hongzhi, Yan Kai, Liu Changfeng, Wang Jianwen, Luo Bin. Ground Robot Relocation Method Based on UAV Point Cloud Map[J]. Journal of System Simulation, 2024, 36(10): 2444-2454.

Figures/Tables 14

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场景	数据组号	局部点云组数量	重定位成功数量	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
工厂	1	73	70	95.89	0.113	0.009	14.377
	2	58	56	96.55	0.144	0.010	14.693
	3	103	100	97.09	0.112	0.011	14.587
	4	63	60	95.24	0.113	0.009	14.238
城市	1	94	93	98.94	0.109	0.008	17.753
	2	130	127	97.69	0.132	0.012	17.682
	3	89	85	95.51	0.125	0.010	17.365
	4	168	162	96.43	0.146	0.013	17.587

算法	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
本文算法	97.08	0.112	0.011 1	14.587
FPFH-RANSAC粗匹配+ICP细匹配	36.89	0.263	0.012 8	20.109
FGR粗匹配+ICP细匹配	90.29	0.108	0.009 8	32.869

高斯噪声方差/m	成功率/%	平均距离误差/m	平均旋转误差/rad	平均耗时/s
0	97.08	0.112	0.011 1	14.587
0.05	97.08	0.115	0.010 9	14.786
0.10	96.11	0.127	0.013 6	17.830
0.15	92.23	0.198	0.013 4	22.634
0.20	88.34	0.277	0.016 4	28.611

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