Laser Localization of Autonomous Vehicle Using an Improved Particle Filter

Abstract:

This article presents an improved Rao-Blackwellized particle filter to overcome particles degeneracy phenomenon and acquire the better localization precision of the autonomous vehicle. The joint posteriori probability density is given that being correlative with the position and pose of the autonomous vehicle and the mark characters of the map. The algorithm utilizes a Markov chain Monte Carlo method with the sampling particle of the target to the resample mechanism of the Rao-Blackwellized particle filter. Simulation results show that the improved algorithm is valid.

• Periodical: Advanced Materials Research (Volume 629)
• Pages: 873-877
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.629.873
• Online since: December 2012
• Authors: Wen Jian Ying, Fu Chun Sun
• Keywords: Autonomous Vehicle, Laser, Localization, Particle Filter (PF)

References

[1] Michael Montemerio, Jan Becker, et al. The Stanford Entry in the Urban Challenge. Journal of Field Robotics. 9, 25 (2008).

[2] Thrun S, Fox D, Burgard W, et al. Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence. 1, 121 (2001).

DOI: 10.1016/s0004-3702(01)00069-8

[3] Fox D, Adapting the sample size in particle Filters through KLD-sampling. The International Journal of Robotics Research. 12, 22 (2003).

DOI: 10.1177/0278364903022012001

[4] Lenser S, Veloso M, Sensor resetting localization for poorly modeled mobile robots. Proceedings of the 2000 IEEE International Conference on Robotics and Automation, (2000)1225-1232.

DOI: 10.1109/robot.2000.844766

[5] Khan Z, Balch T, Dellaert F, MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1, 27 (2005).

DOI: 10.1109/tpami.2005.223

[6] R. Merwe, A. Doucet, N. Freitas, and E. Wan, The Unscented Particle Filter. Dept. Eng., Univ. Cambridge, Cambridge, U.K., Tech. Rep. CUED/F-INFENG/TR 380 (2000).

[7] Wu Er-Yong, Xiang Zhi-Yu, Liu Ji-Lin, Robust Robot Monte Carlo Localization. Acta Automatic Sinica, 8, 34 (2008).

[8] Song Yu, Sun Fu-Chun, Li Qing-Ling. Mobile Robot Monte Carlo Localization Based on Improved Unscented Particle Filter. Acta Automatic Sinica. 6, 36(2010).

DOI: 10.3724/sp.j.1004.2010.00851

[9] Cristina Castejón, Dolores Blanco, and Luis Moreno, Compact Modeling Technique for Outdoor Navigation. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans. 1, 38 (2008).

DOI: 10.1109/tsmca.2007.904786

[10] T. Bailey, Consistency of the EKF-SLAM algorithm, Proc. IEEE/RSJ Int. Conf. Intell. Robots and Syst., (2006).

[11] Kai Lingemanna, Andreas N¨uchtera, High-speed laser localization for mobile robots. Robotics and Autonomous Systems, 51(2005).

[12] Tim Bailey: Mobile Robot Localization and Mapping in Extensive Outdoor Environments. PhD Thesis, The University of Sydney, Australian Centre for Field Robotics, (2002).

[13] Ying Wenjian, Sun Fuchun, Liu Huaping, and Song Yu: A collaborative navigation system for autonomous vehicle in flat terrain. IEEE International Conference on Control and Automation, (2009)1615 –1620.

DOI: 10.1109/icca.2009.5410433