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兵工学报 ›› 2019, Vol. 40 ›› Issue (12): 2399-2406.doi: 10.3969/j.issn.1000-1093.2019.12.002

• 论文 • 上一篇    下一篇

无人平台越野环境下同步定位与地图创建

刘忠泽1,2, 陈慧岩1, 崔星2, 熊光明1, 王羽纯1, 陶溢3   

  1. (1.北京理工大学 机械与车辆学院, 北京 100081; 2.中国北方车辆研究所, 北京 100072;3.北京特种车辆研究所, 北京 100072)
  • 收稿日期:2018-12-15 修回日期:2018-12-15 上线日期:2020-02-14
  • 作者简介:刘忠泽(1993—),男,助理工程师, 硕士。E-mail: zackLiu3714@gmail.com
  • 基金资助:
    装备预先研究项目(995-01020401、41412010302)

Real-time LiDAR SLAM in Off-road Environment for UGV

LIU Zhongze1,2, CHEN Huiyan1, CUI Xing2, XIONG Guangming1, WANG Yuchun1, TAO Yi3   

  1. (1.School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China;2.China North Vehicle Research Institute, Beijing 100072, China; 3.Beijing Special Vehicles Research Institute, Beijing 100072, China)
  • Received:2018-12-15 Revised:2018-12-15 Online:2020-02-14

摘要: 为了满足无人平台在越野环境下碰撞检测以及定位的需求、解决三维点云地图处理计算资源耗费大的问题,设计一种适用于越野环境的同步定位与地图创建方案。提出一种拓扑层次地图,将整个地图划分为多个具有层级结构的三维体素子地图,并以概率方法表示每个体素的状态,进而提取可通行区域。基于三维激光雷达和惯性测量单元,提出一种基于分支定界法的旋转直方图最近邻匹配实时闭环检测方法,并使用Ceres优化稀疏位姿图实现6自由度全局位姿实时优化。越野环境下的实验结果显示:该地图的创建以及可通行区域的提取效果良好,全局定位误差在1 m以内,姿态基本与参考高精度惯性导航系统保持一致;基于该方案提取的可通行区域以及位姿优化结果可满足无人平台实时运动的需求。

关键词: 无人平台, 地图创建, 同步定位, 激光雷达, 6自由度, 越野环境

Abstract: Simultaneous localization and mapping (SLAM) plays a more and more important role in the environment perception system of unmanned ground vehicle (UGV). A hierarchical global topological map is proposed to solve the low real time capability problem during processing of 3D lidar point cloud map, which divides the entire map into many submaps consisting of large numbers of tree-structure-based voxels, and the submaps are organized with the help of topology. The probabilistic methods are used to represent the state of each voxel being occupied/null. A transitable area is extracted based on the submaps for the UGV's path planning. With the help of an inertial measurement unit (IMU) and two 3D lidars, a branch and bound search (BBS)-based loop detection algorithm called RHM-ICP algorithm is proposed to realize a real time 6-degrees-of-freedom global pose optimization with the help of Ceres, which is to process the sparse pose adjustment (SPA). Experimental results obtained from a real large-scale off-road environment shows an effective reduction of lidar odometry pose accumulative error with a global location error of less than 1 m and a good performance of 3D mapping. Key

Key words: unmannedgroundvehicle, mapping, simultaneouslocalization, lidar, sixdegreesoffreedom, off-roadenvironment

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