Turn off MathJax
Article Contents
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.

     

  • loading
  • [1]
    张帆, 胡钊政, 陈佳良, 等.基于Wi-Fi指纹与视觉融合的室内交通定位[J].交通信息与安全, 2019, 37(3):61-69+100. ZHANG Fan, HU Zhaozheng, CHEN Jialiang, et al. Indoor traffic positioning based on Wifi fingerprint and vision fusion[J]. Journal of Transport Information and Safety, 2019, 37(3):61-69+100. (in Chinese)
    [2]
    WOLCOTT R W, EUSTICE R M. Robust LiDAR localization using multiresolution Gaussian mixture maps for autonomous driving[J]. The International Journal of Robotics Research,2017, 36(3):292-319.
    [3]
    MUR-ARTAL R,TARDOS J D. ORB-SLAM2:An open-source SLAM system for monocular stereo and RGB-D cameras[J]. IEEE Transactions on Robotics, 2017, 33(5):1255-1262.
    [4]
    杨东凯, 寇艳红, 吴今培, 等.智能交通系统中的地图匹配定位方法[J].交通运输系统工程与信息, 2003(3):38-43. YANG Dongkai,KOU Yanhong,WU Jinpei,et al. A map-matching method in intelligent transport systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2003, 3(3):38-43. (in Chinese)
    [5]
    ZHANG Ji, SINGH S. LOAM:LiDAR odometry and mapping in real-time[C]. Robotics:Science and Systems Conference,Berkeley,California,USA:University of California, Berkeley, 2014.
    [6]
    KOIDE K. A Portable 3D LIDAR-based system for long-term and wide-area people behavior measurement[J]. International Journal of Advanced Robotic Systems, 2019, 16(2):1-13.
    [7]
    THRUN S, MONTEMERLO M, Dahlkamp H, et al. Stanley:The robot that won the DARPA grand challenge[J]. Journal of Field Robotics, 2006, 23(9):661-692.
    [8]
    ZERMAS D, IZZAT I, PAPANIKOLOPOULOS N. Fast segmentation of 3D point clouds:A paradigm on LiDAR data for autonomous vehicle applications[C]. International Conference on Robotics and Automation(ICRA), Singapore:IEEE, 2017.
    [9]
    彭泽民, 叶青.自然环境下交通标志牌的检测与识别[J].电子测试, 2020(1):45-47+92. PENG Zemin, YE Qing. Detection and identification of traffic signs in natural environment[J]. Electronic Test, 2020(1):45-47+92. (in Chinese)
    [10]
    李游.基于车载激光扫描数据的城市街道信息提取技术研究[D].武汉:武汉大学, 2017. LI You. Research on street information detection from mobile laser scanning data in urban areas[D]. Wuhan:Wuhan University, 2017. (in Chinese)
    [11]
    KUZNETSOVA K, SAMSONOVICHAV.Semantic-map-base dapproach to designing an insight problem solving assistant[J]. ProcediaComputerScience, 2018, 123:258-264.
    [12]
    DUBE R, DUGAS D, STUMM E, et al. SegMatch:Segment based place recognition in 3D point clouds[C]. International Conference on Robotics and Automation(ICRA),Singapore:IEEE, 2017.
    [13]
    LIU Yu, PETILLOT Y, LANE D, et al. Global localization with object-level semantics and topology[C]. International Conference on Robotics and Automation(ICRA), Montreal, QC, Canada:IEEE, 2019.
    [14]
    CHEN X, MILIOTO A, PALAZZOLO E, et al. SuMa++:Efficient LiDAR-based semantic SLAM[C]. International Conference on Intelligent Robots and Systems(IROS), Macau, China:IEEE, 2019.
    [15]
    GOLOVINSKIY A, FUNKHOUSER T. Min-cut based segmentation of point clouds[C]. IEEE International Conference on Computer Vision Workshops, Kyoto, Japan:IEEE, 2009.
    [16]
    刘亚坤, 李永强, 刘会云, 等.基于改进RANSAC算法的复杂建筑物屋顶点云分割[J].地球信息科学学报, 2021, 23(8):1497-1507. LIU Yakun, LI Yongqiang, LIU Huiyun, et al. An improved RANSAC algorithm for point cloud segmentation of complex building roofs[J]. Journal of Geo-information Science, 2021, 23(8):1497-1507. (in Chinese)
    [17]
    AI C, TSAI Y J. Critical assessment of an enhanced traffic sign detection method using mobile LiDAR and INS technologies[J]. Journal of Transportation Engineering, 2015, 141(5):04014096.
    [18]
    PU Shi, RUTZINGER M, VOSSELMAN G, et al. Recognizing basic structures from mobile laser scanning data for road inventory studies[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6):S28-S39.
    [19]
    TAO Qianwen, HU Zhaozheng, HUANG Gang, et al. LiDAR-only vehicle localization based on map generation[C]. Transportation Research Board(TRB), 2019 Annual Meeting, Washing D. C, America:NationalAcademies of USA, 2019.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (6769) PDF downloads(5849) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return