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ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds[J]. Journal of Transport Information and Safety.
Citation: ZHU Yuntao, LI Fei, HU Zhaozheng, WU Huawei. Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds[J]. Journal of Transport Information and Safety.

Intelligent Vehicles Localization Based on Semantic Map Representation from 3D Point Clouds

  • Received Date: 2021-10-14
    Available Online: 2021-12-14
  • In order to improve the accuracy of node localization for intelligent vehicles,an intelligent vehicles localization method based on three-dimensional point clouds semantic map representation is proposed. The method is divided into three parts. Semantic segmentation based on 3D laser point clouds includes ground segmentation,traffic signs segmentation and pole-shaped target segmentation. Semantic map representation for intelligent vehicles uses segmented targets to project. Finally directional projections with weight,semantic roads and semantic codeing are generated. The codeing and global location from high-precision GPS make up representation model. Localization based on semantic representation model includes coarse localization from GPS matching and node localization from semantic coding matching. The experiments are carried out in three road scenes with different length and complexity,and the localization accuracy is 98.5%,97.6% and 97.8%,respectively. The results show that proposed method has high accuracy and strong robustness, which is suitable for different road scenes.


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