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Articles

Vol. 9 (2022)

Analysis of Position and State Estimation of Quadruped Robot Dog Based on Invariant Extended Kalman Filter

DOI
https://doi.org/10.31875/2409-9694.2022.09.03
Submitted
August 30, 2022
Published
2022-08-30

Abstract

Abstract: Compared with the state estimation of quadruped robots based on external sensors such as camera and lidar, the state estimation based on body sensors can provide high-frequency and stable odometer estimation. By analyzing the state estimation methods of the legged robot based on the body sensor, the invariant extended Kalman filter (IEKF) based on the body sensor is determined to conduct the state estimation analysis of the quadruped robot. Through various path tracking experiments in simulation and real environment, the influence of travel speed, travel distance and different steering angles on the position state estimation results was analyzed, and the IEKF model was optimized by compensating the angular velocity. Experiments show that within the set speed range, after adding angular velocity compensation, the position estimation accuracy error of the robot dog is well controlled and is less than 1%.

References

  1. S. Bonnabel, "Left-invariant extended Kalman filter and attitude estimation," 2007 46th IEEE Conference on Decision and Control, 2007; pp. 1027-1032. https://doi.org/10.1109/CDC.2007.4434662
  2. S. Bonnable, P. Martin and E. Salaün, "Invariant Extended Kalman Filter: theory and application to a velocity-aided attitude estimation problem," Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009; pp. 1297-1304. https://doi.org/10.1109/CDC.2009.5400372
  3. Barrau A. Non-linear state error based extended Kalman filters with applications to navigation [D]. Mines Paristech 2015.
  4. M. Reinstein and M. Hoffmann, "Dead Reckoning in a Dynamic Quadruped Robot Based on Multimodal Proprioceptive Sensory Information," in IEEE Transactions on Robotics, 2013; 29(2): 563-571. https://doi.org/10.1109/TRO.2012.2228309
  5. Hutter M, Gehring C, Bloesch M, et al. StarlETH: A compliant quadrupedal robot for fast, efficient, and versatile locomotion[M]//Adaptive Mobile Robotics. 2012: 483-490. https://doi.org/10.1142/9789814415958_0062
  6. Bloesch M, Hutter M, Hoepflinger MA, et al. State estimation for legged robots-consistent fusion of leg kinematics and IMU [J]. Robotics, 2013; 17: 17-24. https://doi.org/10.15607/RSS.2012.VIII.003
  7. Bloesch M, Gehring C, Fankhauser P, Hutter M, Hoepflinger MA and Siegwart R. "State estimation for legged robots on unstable and slippery terrain," 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013; pp. 6058-6064. https://doi.org/10.1109/IROS.2013.6697236
  8. A. Barrau and S. Bonnabel, "The Invariant Extended Kalman Filter as a Stable Observer," in IEEE Transactions on Automatic Control, 2017; 62(4): 1797-1812. https://doi.org/10.1109/TAC.2016.2594085
  9. Hartley R, Ghaffari M, Eustice RM, Grizzle JW. Contact-aided invariant extended Kalman filtering for robot state estimation [J]. The International Journal of Robotics Research, 2020; 39(4): 402-430. https://doi.org/10.1177/0278364919894385
  10. Lin T-Y, Zhang R, Yu J, Ghaffari M. Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events[C]. 5th Annual Conference on Robot Learning 2021.
  11. MAO Jun, FU Hao, CHU Chaoqun, HE Xiaofeng, CHEN Changhao. A Review of Simultaneous Localization and Mapping Based on Inertial-visual-lidar Fusion [J/OL]. Navigation Positioning and Timing: 1-19[2022-07-14]. http://kns.cnki.net/kcms/detail/10.1226.V.20220611.1745.012.html