Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations

  • Lee, Seong-Soo (School of Information and Communication Engineering of Sungkyunkwan University) ;
  • Lee, Suk-Han (School of Information and Communication Engineering of Sungkyunkwan University) ;
  • Kim, Dong-Sung (School of Electronic Engineering, Soongsil University)
  • 발행 : 2006.12.30

초록

Simultaneous Localization and Map Building(SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter(EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.

키워드

참고문헌

  1. D. Kortenkamp, R. P. Bonasso, and R. Murphy, Al-based Mobile Robots: Case Studies of Successful Robot Systems, MIT Press, 1998
  2. C. Thorpe and H. F. Durrant-Whyte, 'Field robots,' Proc. of the 10th Int. Symp. of Robotics Research, Lome, Victoria, Australia, SpringerVerlag, November 2001
  3. M. W. M. Gamini Dissanayake, P. Newman, S. Clark, H. Durrant-Whyte, and M. Csorba, 'A solution to the simultaneous localization and map building (SLAM) problem,' IEEE Trans. on Robotics and Automation, vol. 17, no. 3, pp. 229-241, June 2001 https://doi.org/10.1109/70.938381
  4. J. K. Uhlmann, S. J. Julier, and M. Csorba, 'Nondivergent simultaneous map building and localization using covariance intersection,' Proc. of SPIE, Orlando, FL, vol. 3087, pp. 2-11, April 1997
  5. W. D. Renken, 'Concurrent localization and map building for mobile robots using ultrasonic sensors,' Proc. of IEEE Int. Conf Itell. Robots Syst., vol. 3, pp. 2192-2197, July 1993
  6. R. Smith, M. Self, and P. Cheeseman, 'Estimating uncertain spatial relationships in robotics,' Autonomous Robot Vehicles, Springer-Verlag, pp. 167-193, 1990
  7. J. Leonard and H. F. Durrant-Whyte, 'Simultaneous map building and Localization for an autonomous mobile robot,' Proc. of IEEE Int. Wkshp. Intell. Robots Syst., Osaka, Japan, vol. 3, pp. 1442-1447, November 1991
  8. S. Williams, P. Newman, M. Dissanayake, J. Rosenblatt, and H. Durrant- Whyte, 'A decoup1ed, distributed AUV control architectture,' Proc. of the 31st Int. Symp. Robot, vol. 1, pp. 246-251, May 2000
  9. J. Guivant, E. Nebot, and S. Baiker, 'Localization and map building using laser range sensors in outdoor applications,' Journal of Robotic Systems, vol. 17, pp. 565-583, October 2000 https://doi.org/10.1002/1097-4563(200010)17:10<565::AID-ROB4>3.0.CO;2-6
  10. J. L. Leonard and H. J. S. Feder, 'A computationally efficient method for large-scale concurrent mapping and localization,' Proc. of the 9th Int. Symp. on Robotics Research (ISRR), Utah, USA, pp. 316-321,1999
  11. J. E. Guivant and E. M. Nebot, 'Optimization of the simultaneous localization and map building algorithm for real-time implementation,' IEEE Trans. on Robotics and Automation, vol. 17, pp. 242-257, June 2001 https://doi.org/10.1109/70.938382
  12. S. B. Williams, G. Dissanayake, and H. F. Durrant- Whyte, 'An efficient approach to the simultaneous localization and mapping problem,' Proc. of IEEE Int. Conf on Robotics and Automation, vol. 1, pp. 406-411, May 2002
  13. A. Doucet, N. de Freitas, K. Murphy, and S. Russell, 'Rao-Blackwellised particle filtering for dynamic Bayesian networks,' Proc. of the 16th Conf on Uncertainty in Artificial Intelligence, pp. 176-183,2000
  14. A. Doucet, S. Godsill, and C. Andrieu, 'On sequential Monte Carlo sampling methods for Bayesian filtering,' Statistics and Computing, vol. 10, no. 3, pp. 197-208,2000 https://doi.org/10.1023/A:1008935410038
  15. K. Murphy, 'Bayesian map learning in dynamic environments,' Proc. of Neural Information Processing Systems, pp. 1015-1021, 1999
  16. D. Hahnel, D. Fox, W. Burgard, and S. Thrun, 'An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements,' Proc. of IEEE Int. Conf on Intelligent Robots and Systems, vol. 1, pp. 206-211, October 2003
  17. M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, 'FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably con-verges,' Proc. of IJCAI, pp. 1151-1156,2003
  18. S. Thrun, D. Fox, and W. Burgard, 'Monte Carlo localization with mixture proposal distribution,' Proc. of the AAAI National Conference on Artificial Intelligence, Austin, pp. 859-865, 2000
  19. W. Chieh-Chih, C. Thorpe, and S. Thrun, 'Online simultaneous localization and mapping with detection and tracking of moving objects: Theory and results from a ground vehicle in crowded urban areas,' Proc. of IEEE Int. Conf on Robotics and Automation, vol. 1, pp. 842-849, September 2003
  20. E. A. Wan and R. Van Der Merwe, 'The unscented Kalman filter for nonlinear estimation,' Proc. of Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153-158, October 2000
  21. S. J. Julier, J. K. Uhlmann, and H. F. DurrantWhyte, 'A new approach for filtering non-linear systems,' Proc. of the American Control Conference, vol. 3, pp. 1628-1632, June 1995
  22. W. Yuanxin, H. Dewen, and W. Meiping, 'Unscented Kalman filtering for additive noise case: Augmented versus nonaugmented,' IEEE Signal Processing Letters, vol. 12, pp. 357-360, May 2005 https://doi.org/10.1109/LSP.2005.845592
  23. D. G. Lowe, 'Object recognition from local scale-invariant features,' Proc. of the 7th IEEE Int. Conf on Computer Vision, vol. 2, pp. 1150-1157, September 1999
  24. M. Montemerlo, FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem with Unknown Data Association, Ph D thesis, Carnegie Mellon University, Pittsburgh, PA 15213, July 2003