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Journal of Sensors
Volume 2016, Article ID 8729895, 11 pages
http://dx.doi.org/10.1155/2016/8729895
Research Article

Camera Space Particle Filter for the Robust and Precise Indoor Localization of a Wheelchair

1Universidad Autonoma de San Luis Potosi, 78290 San Luis Potosi, SLP, Mexico
2Gannon University, Erie, PA 16541, USA

Received 19 December 2014; Accepted 25 March 2015

Academic Editor: Changhai Ru

Copyright © 2016 Raul Chavez-Romero 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. R. Grasse, Y. Morère, and A. Pruski, “Assisted navigation for persons with reduced mobility: path recognition through particle filtering (condensation algorithm),” Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 60, no. 1, pp. 19–57, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. S. P. Levine, D. A. Bell, L. A. Jaros, R. C. Simpson, Y. Koren, and J. Borenstein, “The navchair assistive wheelchair navigation system,” IEEE Transactions on Rehabilitation Engineering, vol. 7, no. 4, pp. 443–451, 1999. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. L. Ip, A. B. Rad, Y. K. Wong, Y. Liu, and X. M. Ren, “A localization algorithm for autonomous mobile robots via a fuzzy tuned extended Kalman filter,” Advanced Robotics, vol. 24, no. 1-2, pp. 179–206, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Sprunk, G. D. Tipaldi, A. Cherubini, and W. Burgard, “Lidar-based teach-and-repeat of mobile robot trajectories,” in Proceedings of the 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon (IROS '13), pp. 3144–3149, Tokyo, Japan, November 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Pinto, H. Sobreira, A. P. Moreira, H. Mendonça, and A. Matos, “Self-localisation of indoor mobile robots using multi-hypotheses and a matching algorithm,” Mechatronics, vol. 23, no. 6, pp. 727–737, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Rusdinar, J. Kim, and S. Kim, “Error pose correction of mobile robot for SLAM problem using laser range finder based on particle filter,” in Proceedings of the International Conference on Control, Automation and Systems, Gyeonggi-do, Republic of Korea, 2010.
  7. H. M. La, R. S. Lim, B. B. Basily et al., “Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 6, pp. 1655–1664, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. S. A. Hiremath, G. W. A. M. van der Heijden, F. K. van Evert, A. Stein, and C. J. F. Ter Braak, “Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter,” Computers and Electronics in Agriculture, vol. 100, pp. 41–50, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Del Castillo, S. Skaar, A. Cárdenas, and L. Fehr, “A sonar approach to obstacle detection for a vision-based autonomous wheelchair,” Robotics and Autonomous Systems, vol. 54, no. 12, pp. 967–981, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. M.-H. Li, B.-R. Hong, Z.-S. Cai, S.-H. Piao, and Q.-C. Huang, “Novel indoor mobile robot navigation using monocular vision,” Engineering Applications of Artificial Intelligence, vol. 21, no. 3, pp. 485–497, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Romero, A. Camacho, J. Varona, C. Delgado, and R. Velázquez, “A low-cost electric power wheelchair with manual and vision-based control systems,” in Proceedings of the IEEE AFRICON, pp. 1–6, Nairobi, Kenya, September 2009. View at Publisher · View at Google Scholar
  12. Z. Jia, A. Balasuriya, and S. Challa, “Vision based data fusion for autonomous vehicles target tracking using interacting multiple dynamic models,” Computer Vision and Image Understanding, vol. 109, no. 1, pp. 1–21, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. C. de la Cruz, W. C. Celeste, and T. F. Bastos, “A robust navigation system for robotic wheelchairs,” Control Engineering Practice, vol. 19, no. 6, pp. 575–590, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. Y.-H. Wu, B.-Y. Lu, H.-Y. Chen et al., “The development of M3S-Based GPS navchair and tele-monitor system,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 4052–4055, Shanghai, China, September 2005. View at Scopus
  15. F. Aghili and A. Salerno, “Driftless 3-D attitude determination and positioning of mobile robots by integration of IMU with two RTK GPSs,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 21–31, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “Novel approach to nonlinear/non-gaussian Bayesian state estimation,” IEE Proceedings, Part F: Radar and Signal Processing, vol. 140, no. 2, pp. 107–113, 1993. View at Publisher · View at Google Scholar · View at Scopus
  17. E. T. Baumgartner, An autonomous vision-based mobile robot [Ph.D. thesis], University of Notre Dame, Notre Dame, France, 1992.
  18. J. A. C. Galindo, A vision-guidance strategy to control hybrid holonomic/nonholonomic robots [Ph.D. thesis], University of Notre Dame, Notre Dame, France, 2003.
  19. K. Jung, J. Kim, E. Jung, and S. Kim, “Positioning accuracy improvement of laser navigation using UKF and FIS,” Robotics and Autonomous Systems, vol. 62, no. 9, pp. 1241–1247, 2014. View at Publisher · View at Google Scholar · View at Scopus
  20. C. Pozna, R.-E. Precup, and P. Földesi, “A novel pose estimation algorithm for robotic navigation,” Robotics and Autonomous Systems, vol. 63, no. 1, pp. 10–21, 2015. View at Publisher · View at Google Scholar
  21. G. Del Castillo, S. B. Skaar, and L. Fehr, “Extending teach and repeat to pivoting wheelchairs,” Systemics, Cybernetics and Informatics, vol. 1, no. 1, pp. 55–62, 2003. View at Google Scholar
  22. X. Perrin, R. Chavarriaga, F. Colas, R. Siegwart, and J. D. R. Millán, “Brain-coupled interaction for semi-autonomous navigation of an assistive robot,” Robotics and Autonomous Systems, vol. 58, no. 12, pp. 1246–1255, 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. G. del Castillo, Autonomous, vision-based, pivoting wheelchair with obstacle [Ph.D. thesis], University of Notre Dame, Notre Dame, France, 2004.
  24. D. Simon, Optimal State Estimation, Kalman, H, and Nonlinear Approaches, John Wiley & Sons, Hoboken, NJ, USA, 2006.
  25. J. L. Peralta-Cabezas, M. Torres-Torriti, and M. Guarini-Hermann, “A comparison of Bayesian prediction techniques for mobile robot trajectory tracking,” Robotica, vol. 26, no. 5, pp. 571–585, 2008. View at Publisher · View at Google Scholar · View at Scopus