Localization and mapping in urban area based on 3D point cloud of autonomous vehicles |
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Authors: | WANG Mei-ling LI Yu YANG Yi ZHU Hao and LIU Tong |
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Affiliation: | Key Laboratory of Intelligent Control and Decision of Complex System, School of Automation,Beijing Institute of Technology, Beijing 100081, China |
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Abstract: | In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping (SLAM) algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points (ICP) algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles'' importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles'' poses, which would decrease particle numbers, increase algorithm speed and restrain particles'' impoverishment. The calculation of particles'' importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches. |
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Keywords: | simultaneous localization and mapping (SLAM) Rao-Blackwellized particle filter (RBPF) VoxelGrid filter ICP algorithm Gaussian model urban area |
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