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基于混合地图的护理机器人室内导航方法

张立志 陈殿生 刘维惠

张立志, 陈殿生, 刘维惠等 . 基于混合地图的护理机器人室内导航方法[J]. 北京航空航天大学学报, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325
引用本文: 张立志, 陈殿生, 刘维惠等 . 基于混合地图的护理机器人室内导航方法[J]. 北京航空航天大学学报, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325
ZHANG Lizhi, CHEN Diansheng, LIU Weihuiet al. Care robot indoor navigation method based on hybrid map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325(in Chinese)
Citation: ZHANG Lizhi, CHEN Diansheng, LIU Weihuiet al. Care robot indoor navigation method based on hybrid map[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 991-1000. doi: 10.13700/j.bh.1001-5965.2017.0325(in Chinese)

基于混合地图的护理机器人室内导航方法

doi: 10.13700/j.bh.1001-5965.2017.0325
基金项目: 

北京市科技计划重大项目课题 D141100003614002

详细信息
    作者简介:

    张立志 男, 博士研究生。主要研究方向:机器人移动导航

    陈殿生 男, 博士, 教授, 博士生导师。主要研究方向:助老助残机器人、仿生机器人、机电智能控制技术

    刘维惠 女, 博士研究生。主要研究方向:机械臂运动控制

    通讯作者:

    陈殿生, E-mail: chends@163.com

  • 中图分类号: TP242

Care robot indoor navigation method based on hybrid map

Funds: 

Major Subject of Beijing Science and Technology Program D141100003614002

More Information
  • 摘要:

    护理机器人在室内三维结构化环境下进行导航时,面临着三维建图计算量大且地图中缺乏语义信息的缺点。提出了基于点和平面特征的混合地图构建方法,结合点和平面在地图构建中的优势,并基于该混合地图搭建室内导航系统。首先,快速地提取特征点和特征平面,使用解释树的方法进行数据关联,并使用平滑建图工具构建因子图,进行机器人位姿和路标的联合优化,改进并更新混合地图。然后,搭建室内导航系统,实现了三维障碍物检测、路径规划与运动控制。最后,在走廊环境下进行了室内导航实验,并以由激光雷达构建的二维栅格地图为参考,分析了地图构建效果和机器人定位精度,证明了基于混合地图的室内导航系统在室内结构化环境下的优势。

     

  • 图 1  护理机器人概念设计图

    Figure 1.  Care robot conceptual design sketch

    图 2  护理机器人样机

    Figure 2.  Care robot prototype

    图 3  室内导航系统架构图

    Figure 3.  Indoor navigation system architecture diagram

    图 4  SLAM系统框架图

    Figure 4.  SLAM system framework diagram

    图 5  基于解释树的数据关联

    Figure 5.  Data association based on interpretation tree

    图 6  点到点的距离约束

    Figure 6.  Point to point distance constraint

    图 7  平面与平面间的夹角约束

    Figure 7.  Angle constraint between plane and plane

    图 8  平面路标点云更新

    Figure 8.  Plane landmark point cloud update

    图 9  存在重复平面路标的平面地图

    Figure 9.  Plane map with reduplicated plane landmarks

    图 10  重复平面路标合并后的平面地图

    Figure 10.  Plane map after reduplicated plane landmarks merging

    图 11  三维占栅格地图

    Figure 11.  3D occupancy grid map

    图 12  投影后的二维栅格地图

    Figure 12.  2D occupancy grid map after projection

    图 13  膨胀半径示意图

    Figure 13.  Schematic diagram of inflated radius

    图 14  膨胀操作后的代价地图

    Figure 14.  Costmap after inflation operation

    图 15  全局与局部路径规划

    Figure 15.  Global and local path planning

    图 16  基于点和平面特征的混合地图

    Figure 16.  Hybrid map based on point and plane features

    图 17  GMapping生成的占栅格地图

    Figure 17.  Occupancy grid map generated by GMapping

    图 18  SLAM地图与占栅格地图对比

    Figure 18.  Comparison of SLAM map and occupancy grid map

    图 19  SLAM轨迹与GMapping轨迹对比

    Figure 19.  Comparison of SLAM trajectory and GMapping trajectory

    图 20  SLAM各步骤运算时间

    Figure 20.  Each SLAM step runtime

    图 21  特征提取运算时间

    Figure 21.  Feature extraction runtime

    图 22  建图各步骤运算时间

    Figure 22.  Each mapping step runtime

    表  1  护理机器人样机指标

    Table  1.   Care robot prototype index

    指标 数值
    整机质量/kg 120
    外形尺寸/(m×m×m) 0.9×0.65×1.35
    机械臂自由度 7
    Kinect安装高度/m 1.2
    下载: 导出CSV

    表  2  SLAM实验结果

    Table  2.   SLAM experimental results

    实验结果 数值
    轨迹长度/m 179.74
    持续时间/s 617.53
    数据帧数 6 176
    关键帧数 1 495
    平移误差均值/m 0.097 0
    平移RMSE/m 0.106 3
    旋转误差均值/(°) 1.084 0
    旋转RMSE/(°) 1.194 1
    下载: 导出CSV

    表  3  SLAM各步骤平均运算时间

    Table  3.   Each SLAM step average runtime

    步骤 运算时间/ms
    特征平面提取 17.39
    特征点提取 27.11
    特征平面数据关联 9.66
    特征点数据关联 22.78
    因子图优化 116.07
    地图改进 100.35
    路标更新 193.50
    总计 499.36
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-05-17
  • 录用日期:  2017-07-13
  • 刊出日期:  2018-05-20

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