Volume 39 Issue 6
Dec.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(6): 143-152
ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points[J]. Journal of Transport Information and Safety, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017
Citation: ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points[J]. Journal of Transport Information and Safety, 2021, 39(6): 143-152. doi: 10.3963/j.jssn.1674-4861.2021.06.017
shu

A Localization Method for Intelligent Vehicles Based on Semantic Map Representation Extracted from 3D Cloud Points

doi: 10.3963/j.jssn.1674-4861.2021.06.017
  • 1.

    Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China

  • 2.

    Chongqing Research Institute, Wuhan University of Technology, Wuhan 430063, China

  • 3.

    School of Automotive and Traffic Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei, China

  • Received Date: 2021-09-14
    Available Online: 2022-01-12
    Abstract
  • An intelligent vehicle localization method based on the semantic-map representation of 3D point clouds is proposed to improve the accuracy of node localization for intelligent vehicles. The method is divided into three parts. ① Semantic segmentation based on 3D laser point clouds includes segmentation of ground, traffic sign, and pole-shaped targets. ② Semantic-map representation for intelligent vehicles uses segmented targets to project. Directional projections with weight, semantic roads, and semantic coding are generated. The coding and global location from high-precision GPS make up the representation model. ③ Localization based on a semantic representation model includes coarse positioning from GPS matching and node localization from semantic coding matching. The experiments are performed in three road scenes with different lengths and complexities, and their localization accuracy is 98.5%, 97.6%, and 97.8%, respectively. The results show that the proposed method has high accuracy and strong robustness, suitable for different road scenes.

     

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