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基于多约束因子图优化的无人车定位与建图方法

牛国臣 王瑜

牛国臣, 王瑜. 基于多约束因子图优化的无人车定位与建图方法[J]. 北京航空航天大学学报, 2021, 47(2): 306-314. doi: 10.13700/j.bh.1001-5965.2020.0212
引用本文: 牛国臣, 王瑜. 基于多约束因子图优化的无人车定位与建图方法[J]. 北京航空航天大学学报, 2021, 47(2): 306-314. doi: 10.13700/j.bh.1001-5965.2020.0212
NIU Guochen, WANG Yu. Unmanned vehicle positioning and mapping method based on multi-constraint factor graph optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 306-314. doi: 10.13700/j.bh.1001-5965.2020.0212(in Chinese)
Citation: NIU Guochen, WANG Yu. Unmanned vehicle positioning and mapping method based on multi-constraint factor graph optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(2): 306-314. doi: 10.13700/j.bh.1001-5965.2020.0212(in Chinese)

基于多约束因子图优化的无人车定位与建图方法

doi: 10.13700/j.bh.1001-5965.2020.0212
基金项目: 

中央高校基本科研业务费专项资金 3122019043

天津市科技计划 17ZXHLGX00120

详细信息
    作者简介:

    牛国臣 男, 博士, 副教授, 硕士生导师。主要研究方向: 智能机器人、机场智能与自动化系统

    王瑜 女, 硕士研究生。主要研究方向: 激光SLAM、自主导航系统

    通讯作者:

    牛国臣. E-mail: niu_guochen@139.com

  • 中图分类号: V249.32;TP242

Unmanned vehicle positioning and mapping method based on multi-constraint factor graph optimization

Funds: 

The Fundamental Research Funds for the Central Universities 3122019043

Tianjin Science and Technology Plan Project 17ZXHLGX00120

More Information
  • 摘要:

    针对目前在特定场景下应用的低速无人车定位系统极度依赖全球导航卫星系统(GNSS),存在定位精度不高、漂移误差大、受环境影响严重等问题,提出一种低成本、高精度的无人车定位与建图方法。该方法基于三维激光定位与建图(SLAM)技术。首先,使用点云主成分分析(PCA)实现基于特征匹配的激光里程计;其次,将GNSS位置信息、点云分割聚类得到的地平面和点云聚类特征作为位姿约束分别加入图优化框架,消除激光里程计的累积误差;最后,得到最优位姿和大规模场景的点云地图,以实现无人车的自主定位导航。利用包含大型户外城市街道环境的KITTI数据集对所提出的SLAM算法进行了评估,结果表明:系统在3km运动距离情况下定位偏差可控制在1.5 m以下,在局部精度和全局一致性方面均优于其他里程计系统,为无人车的定位提供了新思路。

     

  • 图 1  系统整体结构

    Figure 1.  Overall structure of system

    图 2  地平面分割方法示意

    Figure 2.  Schematic diagram of ground plane segmentation method

    图 3  点云预处理效果

    Figure 3.  Point cloud pretreatment effect

    图 4  因子图结构

    Figure 4.  Structure of factor graph

    图 5  相对位姿误差和点云序列曲线

    Figure 5.  Curves of RPE and point cloud sequence

    图 6  绝对轨迹误差和点云序列的变化曲线

    Figure 6.  Change curves of ATE and point cloud sequence

    图 7  SLAM系统得到的轨迹与参考轨迹对比

    Figure 7.  Comparison between trajectory obtained by SLAM system and reference trajectory

    图 8  点云地图

    Figure 8.  A point cloud map

    表  1  相对位姿误差对比

    Table  1.   Comparison of Relative Pose Errors (RPE)

    实验结果 LOAM 本文
    短距离 长距离 短距离 长距离
    RPE最大值/m 0.79 9.40 0.81 6.21
    RPE最小值/m 0.09 0.08 0.10 0.08
    RPE平均值/m 0.32 1.98 0.30 0.81
    RPE中值/m 0.29 1.31 0.23 0.73
    RPE RMSE/m 0.39 2.09 0.31 1.05
     数据帧数 1704 4544 1704 4544
     轨迹长度/m 1392 3714 1392 3714
    下载: 导出CSV

    表  2  绝对轨迹误差对比

    Table  2.   Comparison of Absolute Trajectory Errors (ATE)

    实验结果 LOAM 本文
    ATE最大值 39.85 6.44
    ATE最小值 0.03 0.02
    ATE平均值 18.68 3.16
    ATE中值 15.23 2.72
    ATE RMSE 22.17 3.44
    数据帧数 4 544 4 544
    轨迹长度/m 3 714 3 714
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-25
  • 录用日期:  2020-06-19
  • 网络出版日期:  2021-02-20

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