Volume 39 Issue 6
Dec.  2021
Turn off MathJax
Article Contents
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.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [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. doi: 10.3963/j.issn.1674-4861.2019.03.008

    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). doi: 10.3963/j.issn.1674-4861.2019.03.008
    [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. doi: 10.1177/0278364917696568
    [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. doi: 10.1109/TRO.2017.2705103
    [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. doi: 10.3969/j.issn.1009-6744.2003.03.008

    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). doi: 10.3969/j.issn.1009-6744.2003.03.008
    [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. http://www.researchgate.net/publication/331224140_A_Portable_3D_LIDAR-based_System_for_Long-term_and_Wide-area_People_Behavior_Measurement
    [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. doi: 10.1002/rob.20147
    [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. https://www.cnki.com.cn/Article/CJFDTOTAL-WDZC202001015.htm

    PENG Zemin, YE Qing. Detection and identification of traffic signs in natural environment[J]. Electronic Test, 2020(1): 45-47+92. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WDZC202001015.htm
    [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, SAMSONOVICH A V. Semantic-map-base dapproach to designing an insight problem solving assistant[J]. Procedia Computer Science, 2018(123): 258-264. http://www.sciencedirect.com/science/article/pii/S1877050918300413/pdf?md5=139ba648e1b132f94671dd265cc50796&pid=1-s2.0-S1877050918300413-main.pdf
    [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. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202108014.htm

    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). https://www.cnki.com.cn/Article/CJFDTOTAL-DQXX202108014.htm
    [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): 1-12.
    [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. doi: 10.1016/j.isprsjprs.2011.08.006
    [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, Washington, D. C., America: National Academies of USA, 2019.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 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

    Figures(16)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (929) PDF downloads(30) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return