Abstract
This paper focuses on localization and mapping issues for autonomous mobile robots equipped with low-cost 2D lidar in complex environments. Most existing solutions commonly parameterize the robot pose on SE(3) when the robot moves on the rough ground and uses the scan data that may be insufficient or sparse to build the map. In this paper, we first developed the Gaussian Process (GP) to address insufficient scan data for low-precision 2D lidar by enriching the lidar measurements at interest or specific bearing regions. Meanwhile, A new method, based on the graph optimization framework, to solve the non-SE(2) perturbations is proposed, namely SE2-3D constraint, which directly parameterizes the robot pose as SE(2) without ignoring the non-SE(2) perturbations by associating the extended SE(2) pose with map point via lidar measurements. The experimental results indicate that the raw lidar data processed by our method can generate higher quality maps than the original data under the same working conditions. The simulation results verify that the proposed method has higher performance in terms of accuracy than traditional methods. This paper provides a meaningful solution for the broad application of ground mobile robots equipped with low-cost 2D lidar.
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Xi’an Science and Technology Bureau [2017040CG/CG014].
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Wei Chen: contributed the central idea, analysed most of the data, and wrote the initial draft of the paper. Jian Sun: Reviewed and Edited. Ziheng Zhao: designed computer programs. Qiang Zheng: conducted the analyses. all authors discussed the results and revised the manuscript.
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Chen, W., Sun, J., Zhao, Z. et al. Gaussian Processes in Polar Coordinates for Mobile Robot Using SE(2)-3D Constraints. J Intell Robot Syst 103, 72 (2021). https://doi.org/10.1007/s10846-021-01520-0
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DOI: https://doi.org/10.1007/s10846-021-01520-0