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面向煤矿巷道环境的LiDAR与IMU融合定位与建图方法

马艾强 姚顽强 蔺小虎 张联队 郑俊良 武谋达 杨鑫

马艾强,姚顽强,蔺小虎,等. 面向煤矿巷道环境的LiDAR与IMU融合定位与建图方法[J]. 工矿自动化,2022,48(12):49-56.  doi: 10.13272/j.issn.1671-251x.2022070007
引用本文: 马艾强,姚顽强,蔺小虎,等. 面向煤矿巷道环境的LiDAR与IMU融合定位与建图方法[J]. 工矿自动化,2022,48(12):49-56.  doi: 10.13272/j.issn.1671-251x.2022070007
MA Aiqiang, YAO Wanqiang, LIN Xiaohu, et al. Coal mine roadway environment-oriented LiDAR and IMU fusion positioning and mapping method[J]. Journal of Mine Automation,2022,48(12):49-56.  doi: 10.13272/j.issn.1671-251x.2022070007
Citation: MA Aiqiang, YAO Wanqiang, LIN Xiaohu, et al. Coal mine roadway environment-oriented LiDAR and IMU fusion positioning and mapping method[J]. Journal of Mine Automation,2022,48(12):49-56.  doi: 10.13272/j.issn.1671-251x.2022070007

面向煤矿巷道环境的LiDAR与IMU融合定位与建图方法

doi: 10.13272/j.issn.1671-251x.2022070007
基金项目: 国家自然科学基金项目(42001417);国土资源部煤炭资源勘查与综合利用重点实验室项目(KF2021-4)。
详细信息
    作者简介:

    马艾强(1996—),男,陕西榆林人,硕士研究生,主要研究方向为煤矿机器人实时定位与建图,E-mail: aiqiang0125@163.com

  • 中图分类号: TD67

Coal mine roadway environment-oriented LiDAR and IMU fusion positioning and mapping method

  • 摘要: 针对煤矿井下喷浆表面、对称巷道等引起移动机器人自主导航定位与建图失效问题,提出了一种面向煤矿巷道环境的激光雷达(LiDAR)与惯性测量单元(IMU)融合的实时定位与建图方法。首先对原始点云进行分割,利用IMU预积分位姿去除原始点云非线性运动畸变,并对得到的点云进行线、面特征提取。然后将相邻帧的线、面特征进行匹配,在分层位姿估计过程中融合IMU预积分所得到的位姿初值,减少计算迭代次数,提高特征点匹配的精度,解算出当前帧的位姿。最后向因子图中插入局部地图因子、IMU因子、关键帧因子,对位姿进行优化约束,对关键帧与局部地图进行匹配,通过八叉树结构实现地图构建。为验证所提方法的定位性能与建图效果,搭建了Autolabor 、VLP−16 LiDAR和Ellipse−N IMU的实验平台进行验证,并与LeGO−LOAM、LIO−SAM方法进行定性定量对比分析。结果表明:① 在煤矿巷道环境中,面向煤矿巷道环境的LiDAR与IMU融合的实时定位与建图方法三轴方向的绝对定位误差的均值和中值均小于32 cm;对X轴的位姿估计精度最高,其累计误差为1.65 m,位置偏差为2.97 m,建图效果整体良好,建图轨迹未发生漂移;构建的点云地图在完整性和几何结构真实性方面均有着优秀的表现,可以直观反映巷道环境的实际情况,具有良好的鲁棒性。这是因为点云匹配之后进行了分层位姿估计,多因子优化可有效降低全局累计误差,对轨迹精度和地图的一致性提升具有重要作用。② 在楼道走廊环境中,面向煤矿巷道环境的LiDAR与IMU融合的实时定位与建图方法三轴的误差均小于1.01 m,误差均值为5~15 cm,误差范围小,精度高;累计位置偏差仅为1.67 m;完整性与环境匹配均有良好的性能。这是由于通过增加关键帧因子,插入因子图对其新增节点相关变量进行优化,降低了位姿估计漂移,定位与建图精度相对较高。

     

  • 图  1  SLAM基本框架

    Figure  1.  SLAM basic framework

    图  2  分层位姿估计

    Figure  2.  Hierarchical pose estimation

    图  3  关键帧选取

    Figure  3.  Key frames selection

    图  4  多因子图优化

    Figure  4.  Multi-factor graph optimization

    图  5  实验平台

    Figure  5.  Experimental platform

    图  6  场景A(参考点A0—A9)和场景B(参考点B0—B5)的坐标真值

    Figure  6.  Coordinate ture value of scenario A (reference points A0-A9) and scenario B (reference points B0-B5)

    图  7  场景A、B的绝对定位误差分布

    Figure  7.  Absolute positioning error distribution of scenarios A and B

    图  8  煤矿巷道场景A 中3种方法建图结果与轨迹

    Figure  8.  Mapping results and tracks of three methods in coal mine roadway scenarios A

    图  9  楼道走廊场景B中3种方法建图结果与轨迹

    Figure  9.  Mapping results and tracks of three methods in corridor scenarios B

    表  1  实测数据

    Table  1.   Measured data

    数据集地物
    类型
    移动
    目标
    轨迹
    长度/m
    高差/cm最大位移
    速度/(m·s−1
    煤矿巷道A防护装置279150.8
    楼道走廊B室内建筑少量221101.5
    下载: 导出CSV

    表  2  3种方法的累计误差

    Table  2.   Cumulative error of there methods

    场景方法ΔX/mΔY/mΔZ/m位置
    偏差/m
    ALeGO−LOAM13.1016.5644.3949.16
    LIO−SAM2.101.883.044.15
    本文方法1.651.751.742.97
    BLeGO−LOAM1.421.132.332.95
    LIO−SAM2.213.151.053.99
    本文方法0.940.941.011.67
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-07-04
  • 修回日期:  2022-11-26
  • 网络出版日期:  2022-08-09

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