Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 967032, 7 pages
http://dx.doi.org/10.1155/2014/967032
Research Article

Multirobot FastSLAM Algorithm Based on Landmark Consistency Correction

1School of Electrical and Electrical Engineering, East China Jiaotong University, Nanchang 330013, China
2Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received 19 December 2013; Revised 16 April 2014; Accepted 20 April 2014; Published 12 May 2014

Academic Editor: Piermarco Cannarsa

Copyright © 2014 Shi-Ming Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Julier and J. K. Uhlmann, “Using covariance intersection for SLAM,” Robotics and Autonomous Systems, vol. 55, no. 1, pp. 3–20, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Leonard and P. Newman, “Consistent, convergent and constant-time SLAM,” in Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1143–1150, 2003.
  3. B. J. Xiao, K. Xu, X. Chen et al., “A wavelet transform assisted extended kalman filter-based approach for simultaneous localization and mapping problems,” Sensor Letters, vol. 10, no. 8, pp. 1814–1818, 2012. View at Google Scholar
  4. P. Fazli, A. Davoodi, and K. M. Alan, “Multi-robot repeated area coverage,” Autonomous Robots, vol. 34, no. 4, pp. 251–276, 2013. View at Google Scholar
  5. Y. F. Cai, Z. M. Tang, and C. X. Zhao, “A new approach of formation navigation derived from multi-robots cooperative online FastSLAM,” Journal of Control Theory and Applications, vol. 10, no. 4, pp. 451–457, 2012. View at Google Scholar
  6. S. Thrun and Y. F. Liu, “Multi-robot SLAM with sparse extended information filers,” Springer Tracts in Advanced Robotics, vol. 15, no. 2, pp. 254–266, 2005. View at Google Scholar
  7. M. Pfingsthorn, B. Slamet, and A. Visser, “A scalable hybrid multi-robot SLAM method for highly detailed maps,” Lecture Notes in Computer Science, vol. 50, no. 1, pp. 457–464, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. G. P. Huang, N. Trawny, A. I. Mourikis, and S. I. Roumeliotis, “Observability-based consistent EKF estimators for multi-robot cooperative localization,” Autonomous Robots, vol. 30, no. 1, pp. 99–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Yuan, Y.-L. Huang, T. Tao, and B.-Y. Xi, “Multi-robot active simultaneous localization and mapping based on local submap approach,” Robot, vol. 31, no. 2, pp. 97–103, 2009. View at Google Scholar · View at Scopus
  10. A. Howard, “Multi-robot simultaneous localization and mapping using particle filters,” International Journal of Robotics Research, vol. 25, no. 12, pp. 1243–1256, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. F. Cai, Z. M. Tang, and C. X. Zhao, “An improved algorithm of multi-robots cooperative online fastSLAM,” Journal of Computer Research and Development, vol. 49, no. 4, pp. 763–769, 2012. View at Google Scholar
  12. A. Gil, Ó. Reinoso, M. Ballesta, and M. Juliá, “Multi-robot visual SLAM using a Rao-Blackwellized particle filter,” Robotics and Autonomous Systems, vol. 58, no. 1, pp. 68–80, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. H. C. Lee, S. H. Lee, H. Choi et al., “Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses,” Robotica, vol. 30, no. 2, pp. 205–220, 2012. View at Google Scholar
  14. M. Montemerlo, S. Thrun, S. T. D. Koller et al., “FastSLAM2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges,” in Proceeings of the International Conference on Artificial Intelligence, pp. 1151–1156, IJCAI, California, Calif, USA, 2003.
  15. Q. Zhang, J. C. Ma, and Q. Liu, “Improved fastslam2.0 based on the H4 filter for intelligent mobile robot,” Research Journal of Applied Sciences, Engineering and Technology, vol. 4, no. 16, pp. 2748–2754, 2012. View at Google Scholar
  16. T. Bailey, J. Nieto, and E. Nebot, “Consistency of the FastSLAM algorithm,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '06), pp. 424–429, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. S. I. Birbil and S.-C. Fang, “An electromagnetism-like mechanism for global optimization,” Journal of Global Optimization, vol. 25, no. 3, pp. 263–282, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. S. I. Birbil, S.-C. Fang, and R.-L. Sheu, “On the convergence of a population-based global optimization algorithm,” Journal of Global Optimization, vol. 30, no. 2-3, pp. 301–318, 2004. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Wang, W. Yang, and H.-B. Shi, “Consensus-based filtering algorithm with packet-dropping,” Acta Automatica Sinica, vol. 36, no. 12, pp. 1689–1696, 2010. View at Publisher · View at Google Scholar · View at Scopus