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

  • Article
  • | |
  • Metrics
  • |
  • Reference [15]
  • |
  • Related
  • |
  • Cited by
  • | |
  • Comments
    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.

    Reference
    [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.
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:71
  • PDF: 0
  • HTML: 0
  • Cited by: 0
History
  • Received:January 05,2022
  • Revised:June 08,2022
  • Adopted:June 08,2022
Article QR Code

Address:No. 219, Ningliu Road, Nanjing, Jiangsu Province

Postcode:210044

Phone:025-58731025