The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
권호 표지
DOI QR코드

DOI QR Code

Improvement of SLAM Using Invariant EKF for Autonomous Vehicles

Invariant EKF를 사용한 자율 이동체의 SLAM 개선

  • 정다빈 (조선대학교 대학원 전자공학과) ;
  • 고낙용 (조선대학교 전자공학부) ;
  • 정준혁 (조선대학교 대학원 제어계측공학과) ;
  • 변재영 (조선대학교 정보통신공학부) ;
  • 황석승 (조선대학교 전자공학부) ;
  • 김태운 (한림대학교 SW융합대학)
  • Received : 2020.02.12
  • Accepted : 2020.04.15
  • Published : 2020.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes an implement of Simultaneous Localization and Mapping(SLAM) in two dimensional space. The method uses Invariant Extended Kalman Filter(IEKF), which transforms the state variables and measurement variables so that the transformed variables constitute a linear space when variables called the invariant quantities are kept constant. Therefore, the IEKF guarantees convergence provided in the invariant quantities are kept constant. The proposed IEKF approach uses Lie group matrix for the transformation. The method is tested through simulation, and the results show that the Kalman gain is constant as it is the case for the linear Kalman filter. The coherence between the estimated locations of the vehicle and the detected objects verifies the estimation performance of the method.

본 논문은 2차원 공간에서 SLAM(: Simultaneous Localization and Mapping)의 구현을 설명한다. 본 논문에서 사용한 방법은 불변량이라고 하는 변수가 일정하게 유지 될 때 변환된 변수가 선형 공간을 구성하도록 상태 변수와 측정 변수를 변환하는 IEKF(: Invariant extended Kalman filter)를 사용한다. 따라서, IEKF는 불변량이 일정하게 유지되는 경우 수렴을 보장한다. 제안된 IEKF 접근법 중 변환을 하는 과정에서는 리군(Lie group) 행렬을 사용한다. 이 방법은 시뮬레이션을 통해 테스트 되었으며 결과는 선형 칼만 필터의 경우와 마찬가지로 칼만 이득이 일정하다는 것을 보여준다. 즉, 시뮬레이션 결과 이동체의 추정된 위치와 검출된 물체들 사이의 일관성을 보였다.

Keywords

References

  1. N. Ko and J. Chung, "Implementation of SLAM in 2-Dimensional Environment Using Invariant EKF," In Proc. Institute of Control Robotics and Systems, May 2018, pp. 10-11.
  2. G. Song, N. Ko, and H. Choi, "Attitude Estimation of Unmanned Vehicles Using Unscented Kalman Filter," J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 1, Feb. 2019, pp. 265-274. https://doi.org/10.13067/JKIECS.2019.14.1.265
  3. T. Kim and S. Hwang, "Cascade AOA Estimation Using Uniform Rectangular Array Antenna," J. of the Korea Institute of Electronic Communication Sciences, vol. 13, no. 5, Oct. 2018, pp. 923-930. https://doi.org/10.13067/JKIECS.2018.13.5.923
  4. Y. Cho, J. Hwang, and H. Lee "A Position Estimation of Quadcopter Using EKF-SLAM," J. of institute of Korean Electrical and Electronics Engineers, vol. 19, no. 4, Dec. 2015, pp. 557-565.
  5. S. Lee, J. Oh, Y. Cho, and B. Lee, "Loop Closing and Place Recognition System for Robust Cooperative SLAM," In Proc. Korean Institute of Electrical Engineers, Oct, 2013, pp. 167-168.
  6. J. Hyun, H. Lim, and H. Myung, "Graph SLAM Algorithm Exploiting Geomagnetic Field and UWB Signal," In Proc. J. of Institute of Control Robotics and Systems, May 2018, pp. 345-346.
  7. A. Barrau and S. Bonnabel, "An EKF-SLAM algorithm with consistency properties," arXiv preprint arXiv:1510.06263, 2015.
  8. T. Kim, N. Ko, and S. Noh, "Simultaneous Estimation of Landmark Location and Robot Pose Using Particle Filter Method," International Journal of Fuzzy Logic and Intelligent Systems, vol. 22, no. 3, 2012, pp. 353-360.
  9. N. Ko, J. Chung, and D. Jeong, "The Implementation of Graph-based SLAM Using General Graph Optimization," J. of the Korea Institute of Electronic Communication Sciences, vol. 14, no. 4, Aug. 2019, pp. 637-644. https://doi.org/10.13067/JKIECS.2019.14.4.637
  10. M. Kaess, A. Ranganathan and, F. Dellaert, "iSAM: Incremental Smoothing and Mapping," IEEE Transactions on Robotics (TRO), vol. 24, no. 6, 2008, pp. 1365-1378. https://doi.org/10.1109/TRO.2008.2006706
  11. G. Grisetti, C. Stachniss, and W. Burgard, "Nonlinear Constraint Network Optimization for Efficient Map Learning,"IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 3, 2009, pp. 428-439. https://doi.org/10.1109/TITS.2009.2026444
  12. J. Chung, "Estimation of Position, Attitude, and Map for Navigation of Unmanned Vehicles," Master's Thesis, Chosun University Graduate School, 2019.
  13. M. Rhudy and Y. Gu, "Understanding Nonlinear Kalman Filters, Part I: Selection between EKF and UKF," Interactive Robotics Letters, West Virginia University, Jun. 2013.
  14. N. Ko, G. Song, W. Yoon, and T. Kim, "Improvement of Extended Kalman Filter Using Invariant Extended Kalman Filter," 18th International Conference on Control, Automation and Systems(ICCAS 2018), Pyeong Chang-Gang won, Korea, Oct. 2018, pp. 948-950.
  15. S. Seong, "Design of an Invariant EKF for Nonlinear Measurement Equations and its Application to a Mobile Robot," J. of Institute of Control Robotics and Systems, Jun. 2019, pp. 512-518. https://doi.org/10.5302/j.icros.2018.18.0019