LINS-GNSS:滤波与优化耦合的GNSS/INS/LiDAR巡检机器人定位方法
作者:
作者单位:

1.云南电网有限责任公司电力科学研究院;2.上海交通大学

基金项目:

卫星技术电力应用综合试验场构建与环境广域智能监测技术研究及应用(YNKJXM20191246)


LINS-GNSS: Filter and Optimization Coupled GNSS/INS/LiDAR Positioning Method for Inspection Robot Localization
Author:
Affiliation:

1.Joint Laboratory of power remote sensing technology,Electric Power Research Institute,Yunnan Power Grid Company ltd;2.Shanghai Key Laboratory of Navigation and Location-based Services,Shanghai Jiao Tong University;3.Department of Electrical Engineering,Shanghai Jiao Tong University

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    摘要:

    过去几年中,机器人已经成为变电站巡检的一种重要手段,为了能够更加灵活的执行巡检任务,非固定线路的机器人巡检技术越来越受到关注。如何在复杂的变电站环境中实现高精度的定位是其中需要解决的核心问题。单一传感器难以满足变电站可靠定位的要求,因此,本文设计了多传感器融合的LINS-GNSS定位方法。其前端基于迭代误差状态卡尔曼滤波框架将激光雷达和惯性导航进行紧耦合,在每次迭代中生成新的特征对应关系递归地校正估计状态。后端使用因子图优化的方法将卫星导航的定位结果与LiDAR-SLAM后端输出的定位结果松耦合。优化过程中先将局部坐标系与全局坐标系对齐,再将卫星导航的位置约束作为先验边添加到后端的因子图中,最后将定位结果在全局坐标系下输出。为了评估LINS-GNSS系统在变电站环境中的性能,本文其进行了实际场景的外场测试。实验结果表明,LINS-GNSS系统在变电站环境中可以达到优于1m的定位精度。

    Abstract:

    In the past few years, robots have become an important means of substation inspection, and robotic inspection technology for non-fixed lines has received increasing attention in order to perform inspection tasks more flexibly. How to achieve high-precision positioning in complex substation environments is one of the core problems to be solved. It is difficult for a single sensor to meet the requirements of reliable positioning in substations, therefore, this paper designs a multi-sensor fusion LINS-GNSS positioning method. Its front-end tightly couples LiDAR and inertial navigation based on an iterative error-state Kalman filter framework, which recursively corrects the estimated state by generating new feature correspondences in each iteration. The back-end uses a factor graph optimization approach to loosely couple the localization results from the satellite navigation with the localization results output from the LiDAR-SLAM back-end. The optimization process first aligns the local coordinate system with the global coordinate system, then adds the position constraints of the GNSS as a priori edge to the factor graph in the back-end, and finally outputs the positioning results in the global coordinate system. In order to evaluate the performance of the LINS-GNSS system in the substation environment, this paper conducted field tests of real scenarios. The experimental results show that the LINS-GNSS system can achieve a positioning accuracy better than 1m in the substation environment.

    参考文献
    [1] Groves P D. Principles of GNSS, inertial, and multisensor integrated navigation systems, [Book review][J]. IEEE Aerospace and Electronic Systems Magazine, 2015, 30(2): 26-27.
    [2] Barbour N M. Inertial navigation sensors[R]. Charles Stark Draper Lab Inc Cambridge Ma, 2010.
    [3] Wen W, Zhang G, Hsu L T. Exclusion of GNSS NLOS receptions caused by dynamic objects in heavy traffic urban scenarios using real-time 3D point cloud: An approach without 3D maps[C]//2018 IEEE/ION Position, Location and Navigation Symposium (PLANS). IEEE, 2018: 158-165.
    [4] Qin T, Cao S, Pan J, et al. A general optimization-based framework for global pose estimation with multiple sensors[J]. arXiv preprint arXiv:1901.03642, 2019.
    [5] 高翔,张涛,刘毅,等. 视觉SLAM十四讲:从理论到实践[M]. 北京:电子工业出版社,2017:17-21.
    [6] Chen C , Pei L , Xu C , et al. Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph[J]. China Satellite Navigation Conference (CSNC) 2019 Proceedings, 2019.
    [7] Hsu L T. Analysis and modeling GPS NLOS effect in highly urbanized area[J]. GPS solutions, 2018, 22(1): 1-12.
    [8] Hyyppa, Juha, Wang, et al. Feasibility Study of Using Mobile Laser Scanning Point Cloud Data for GNSS Line of Sight Analysis[J]. Mobile information systems, 2017, 2017(Pt.2):5407605.1.
    [9] Wen W. 3D LiDAR Aided GNSS and Its Tightly Coupled Integration with INS Via Factor Graph Optimization[C]//Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020). 2020: 1649-1672.
    [10] Li T , Pei L , Xiang Y , et al. P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment[J]. IEEE Sensors Journal, 2020.
    [11] 鄂盛龙,周刚,谭理庆,罗颖婷,许海林.变电站环境下GNSS接收机性能及观测数据质量分析[J].全球定位系统,2020,45(04):36-41+48.
    [12] Qin C, Ye H, Pranata C E, et al. LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020: 8899-8906.
    [13] J Solà. Quaternion kinematics for the error-state Kalman filter[J]. 2017.
    [14] Shan T, Englot B, Meyers D, et al. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping[J]. arXiv preprint arXiv:2007.00258, 2020.
    [15] 22 Lv J, Xu J, Hu K, et al. Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation[J]. arXiv preprint arXiv:2007.14759, 2020.
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文刚,周仿荣,李涛,马御棠,裴凌,刘亚东,钱国超,潘浩. LINS-GNSS:滤波与优化耦合的GNSS/INS/LiDAR巡检机器人定位方法[J].南京信息工程大学学报,,():

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  • 收稿日期:2022-01-05
  • 最后修改日期:2022-06-08
  • 录用日期:2022-06-08

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