| 引用本文: | 胡钊政,李飞,陶倩文,陈佳良.三维点云极化地图表征模型与智能车定位方法[J].哈尔滨工业大学学报,2021,53(8):103.DOI:10.11918/202005106 |
| HU Zhaozheng,LI Fei,TAO Qianwen,CHEN Jialiang.Localization method for intelligent vehicles based on map representation from 3D point cloud polarization[J].Journal of Harbin Institute of Technology,2021,53(8):103.DOI:10.11918/202005106 |
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| 摘要: |
| 为提高智能车定位精度提出一种基于三维点云极化地图表征模型的定位方法。该模型以点云极化图为节点,利用高精度GPS(Global Positioning System)和欧拉角实现该节点的全局位置表征；从极化图中提取点云的二维与三维特征,实现该节点的多尺度特征表征；通过一系列极化节点实现道路场景的数值描绘与虚拟重构。定位过程中,通过对实时获取的三维激光点云进行极化表征并与地图节点进行多尺度特征匹配实现智能车的地图定位。具体而言,首先根据待定位智能车GPS信号的稳定情况选用GPS匹配或者拓扑定位筛选地图节点并获取定位候选集,完成初定位；其次运用点云二维特征匹配结果从定位候选集中检测距离待定位智能车最近的地图节点,完成节点级定位；最后利用点云三维特征匹配结果与最近地图节点的全局位置计算智能车位姿,完成度量级定位。实验在两种典型场景下进行,节点定位准确率98.7%,平均定位误差21.4 cm,最大定位误差42.9 cm。结果表明,本文算法满足智能车高精度定位需求,且鲁棒性强、成本低、计算过程简单。 |
| 关键词: 智能交通 极化地图 场景表征 地图定位 数据融合 智能车定位 |
| DOI：10.11918/202005106 |
| 分类号:U495 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFB1600801)；重庆市自然科学基金(cstc2020jcyj-msxmX0978)；武汉市科技局项目(5,3,2020010602012003) |
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| Localization method for intelligent vehicles based on map representation from 3D point cloud polarization |
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HU Zhaozheng1,LI Fei1,2,TAO Qianwen1,CHEN Jialiang1
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(1.Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China; 2.Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China)
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| Abstract: |
| A method to improve the localization accuracy of intelligent vehicles was proposed by using map representation model from three-dimensional (3D) point cloud polarization. The point cloud polarization image was used as the node in this model, and the global position representation of the node could be realized through high-precision Global Positioning System (GPS) and Euler angle. The 2D and 3D features of the point cloud were then extracted from the polarization image to realize the multi-scale feature representation of the node, and the numerical description and virtual reconstruction of the road scene were realized through a series of polarization nodes. During the localization process, the 3D laser point cloud was in real-time acquired for polarization representation, and multi-scale feature matching was carried out with map nodes to realize the map localization of the intelligent vehicles. Specifically, map nodes were first filtered through GPS matching or topological localization based on the stability condition of the GPS signal of the intelligent vehicle, and a localization candidate set was obtained for the coarse localization. Then, the nearest map node was detected by matching the 2D point cloud features of the polarization image from the candidate set to complete the node localization. Finally, for the metric localization, the position of the intelligent vehicle was calculated by using the 3D point cloud feature matching results and the global position of the nearest map node. The experiment was carried out in two typical scenarios. The node localization accuracy rate was 98.7%, the average localization error was 21.4 cm, and the maximum localization error was 42.9 cm. Results show that the proposed algorithm has the advantages of high positioning accuracy, strong robustness, low cost, and simple calculation process. |
| Key words: intelligent transportation polarization map scene representation map localization data fusion localization for intelligent vehicles |
