[1]宋锐,方勇纯,刘辉.基于LiDAR/INS的野外移动机器人组合导航方法[J].智能系统学报,2020,15(4):804-810.[doi:10.11992/tis.202008026]
SONG Rui,FANG Yongchun,LIU Hui.Integrated navigation approach for the field mobile robot based on LiDAR/INS[J].CAAI Transactions on Intelligent Systems,2020,15(4):804-810.[doi:10.11992/tis.202008026]
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SONG Rui,FANG Yongchun,LIU Hui.Integrated navigation approach for the field mobile robot based on LiDAR/INS[J].CAAI Transactions on Intelligent Systems,2020,15(4):804-810.[doi:10.11992/tis.202008026]
基于LiDAR/INS的野外移动机器人组合导航方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第4期
页码:
804-810
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2020-07-05
- Title:
- Integrated navigation approach for the field mobile robot based on LiDAR/INS
- Keywords:
- mobile robot; SLAM (simultaneous localization and mapping); pose estimation; tightly coupled; nonlinear; inertial navigation; integrated navigation system; data fusion
- 分类号:
- TP242
- 摘要:
- 移动机器人在地形复杂等野外环境跨区域运动时，机器人运动特性和环境特征变化更为明显，由此引起的点云畸变和特征点稀疏等问题尤为突出，有必要结合传感器标定误差、车轮打滑和车体颠簸等因素进一步改进机器人的位姿估计精度。本文对基于LiDAR/INS的移动机器人环境建模和自主导航方法展开研究，针对LeGO-LOAM等在处理车体姿态快速变化时的性能退化问题，提出一种适用于野外移动机器人运动特性的点云特征分析和多传感融合方法，利用IMU的预积分与LiDAR的scan-to-map构成优化函数，进而迭代更新机器人的位姿。野外环境实验结果表明，当机器人以较高速度做转弯运动或在短时间内多次转向时，本文所提方法仍可以为优化提供良好的初值估计，相比LeGO-LOAM等方法具有更高的位姿估计精度。
- Abstract:
- When the mobile robot moves in the large scale field with complex terrain, the motion and environmental characteristics changes dramatically, and the problems of motion distortion in point clouds and the sparse of feature points become prominently. Hence, it is necessary to improve the estimation accuracy of position and states from aspects of the calibration of sensors’ error, wheel-slip and bumpy terrain. The environmental modeling and autonomous navigation of the field mobile robot based on LiDAR/INS (Inertial Navigation System) are studied in this paper. Aiming at the degradation problem of LeGO-LOAM when the vehicle state changes dramatically, the strategy of point cloud feature extraction and sensor fusion are proposed to adapt the motion characteristics of the field mobile robot, the optimization function is composed of IMU pre-integration and scan-to-map of LiDAR, then the state of robot is updated. Finally, the field experiments are implemented when the robot makes a fast turning or steering in limited time, the proposed method are validated to provide more accurate estimation of initial values and states compared to LeGO-LOAM related approaches.
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备注/Memo
收稿日期:2020-08-20。
基金项目:国家自然科学基金项目（61903202），国家重点研发计划项目（2018YFB1307503，2018YFB1309003）
作者简介:宋锐，助理研究员，博士，主要研究方向为环境感知与移动机器人SLAM。主持国家自然科学基金项目1项，发表学术论文10余篇;方勇纯，教授，博士生导师，中国自动化学会理事，南开大学人工智能学院院长，人工智能学会智库首批专家。主要研究方向为机器人视觉控制、欠驱动吊运系统控制、仿生机器人运动控制和微纳米操作。主持国家重点研发计划项目、国家基金重点项目、“十二五”国家技术支撑计划课题、国家基金仪器专项等项目。获吴文俊人工智能自然科学奖一等奖、天津市专利奖金奖、天津市自然科学一等奖、高等教育教学成果一等奖等多项奖励，发表学术论文 100 余篇;刘辉，硕士研究生，主要研究方向为移动机器人SLAM
通讯作者:方勇纯.E-mail:fangyc@nankai.edu.cn
更新日期/Last Update:
2020-07-25