Journal of System Simulation
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.
Recommended Citation
Huang, Hongzhi; Yan, Kai; Liu, Changfeng; Wang, Jianwen; and Luo, Bin
(2024)
"Ground Robot Relocation Method Based on UAV Point Cloud Map,"
Journal of System Simulation: Vol. 36:
Iss.
10, Article 19.
DOI: 10.16182/j.issn1004731x.joss.23-0751
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss10/19
First Page
2444
Last Page
2454
CLC
TP242
Recommended Citation
Huang Hongzhi, Yan Kai, Liu Changfeng, et al. Ground Robot Relocation Method Based on UAV Point Cloud Map[J]. Journal of System Simulation, 2024, 36(10): 2444-2454.
DOI
10.16182/j.issn1004731x.joss.23-0751
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