Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 238131, 14 pages
http://dx.doi.org/10.1155/2015/238131
Research Article

Sufficient Condition for Estimation in Designing Filter-Based SLAM

1Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan Campus, 26600 Pekan, Pahang, Malaysia
2Department of System Design Engineering, School of Integrated Design Engineering, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

Received 29 April 2014; Accepted 29 July 2014

Academic Editor: Tofigh Allahviranloo

Copyright © 2015 Nur Aqilah Othman 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. H. Wang, G. Fu, J. Li, Z. Yan, and X. Bian, “An adaptive UKF based SLAM method for unmanned underwater vehicle,” Mathematical Problems in Engineering, vol. 2013, Article ID 605981, 12 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  2. M. E. West and V. L. Syrmos, “Navigation of an autonomous underwater vehicle (AUV) using robust SLAM,” in Proceedings of the IEEE International Conference on Control Applications (CCA '06), pp. 1801–1806, Munich, Germany, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Nüchter, H. Surmann, K. Lingemann, J. Hertzberg, and S. Thrun, “6D SLAM with an application in autonomous mine mapping,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1998–2003, New Orleans, La, USA, April 2004. View at Scopus
  4. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, The MIT Press, Cambridge, Mass, USA, 2005.
  5. R. Zlot and M. Bosse, “Efficient large-scale 3D mobile mapping and surface reconstruction of an underground mine,” in Proceedings of the Field and Service Robotics, pp. 479–493, January 2014.
  6. P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, “RGB-D mapping: using Kinect-style depth cameras for dense 3D modeling of indoor environments,” International Journal of Robotics Research, vol. 31, no. 5, pp. 647–663, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Forster, S. Lynen, L. Kneip, and D. Scaramuzza, “Collaborative monocular SLAM with multiple micro aerial vehicles,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3962–3970, Tokyo, Japan, November 2013.
  8. M. T. Lazaro, L. M. Paz, P. Pinies, J. A. Castellanos, and G. Grisetti, “Multi-robot SLAM using condensed measurements,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1069–1076, Tokyo, Japan, November 2013.
  9. S. Saeedi, L. Paull, M. Trentini, and H. Li, “Neural network-based multiple robot simultaneous localization and mapping,” IEEE Transactions on Neural Networks, vol. 22, no. 12, pp. 2376–2387, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Çelik and A. K. Somani, “Monocular vision SLAM for indoor aerial vehicles,” Journal of Electrical and Computer Engineering, vol. 2013, Article ID 374165, 15 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “MonoSLAM: real-time single camera SLAM,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052–1067, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Chatterjee and F. Matsuno, “A neuro-fuzzy assisted extended kalman filter-based approach for simultaneous localization and mapping (SLAM) problems,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 5, pp. 984–997, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. C. Fu, M. A. Olivares-Mendez, R. Suarez-Fernandez, and P. Campoy, “Monocular visual-inertial SLAM-based collision avoidance strategy for fail-safe UAV using fuzzy logic controllers,” Journal of Intelligent Robotic Systems, vol. 73, no. 1–4, pp. 513–533, 2014. View at Google Scholar
  14. H. Ahmad and T. Namerikawa, “Intermittent measurement in robotic localization and mapping with FIM statistical bounds,” IEEJ Transactions on Electronics, Information and Systems, vol. 131, no. 6, pp. 1223–1232, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Muraca, P. Pugliese, and G. Rocca, “An extended Kalman Filter for the state estimation of a mobile robot from intermittent measurements,” in Proceedings of the 16th Mediterranean Conference on Control and Automation (MED '08), pp. 1850–1855, Ajaccio Corsica, France, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. P. M. Lynch and J. F. Figueroa, “Position estimation with intermittent measurements,” in Proceedings of the American Control Conference, pp. 2280–2285, Boston, Mass, USA, June 1991. View at Scopus
  17. M. W. M. Gamini Dissanayake, P. Newman, S. Clark, H. F. Durrant-Whyte, and M. Csorba, “A solution to the simultaneous localization and map building (SLAM) problem,” IEEE Transactions on Robotics and Automation, vol. 17, no. 3, pp. 229–241, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Nieto, T. Bailey, and E. Nebot, “Can-SLAM: combining EKF-SLAM and scan correlation,” Field and Service Robotics, vol. 25, pp. 167–178, 2006. View at Publisher · View at Google Scholar
  19. S. Huang and G. Dissanayake, “Convergence and consistency analysis for extended Kalman filter based SLAM,” IEEE Transactions on Robotics, vol. 23, no. 5, pp. 1036–1049, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. J. A. Castellanos, R. Martinez-Cantin, J. D. Tardós, and J. Neira, “Robocentric map joining: improving the consistency of EKF-SLAM,” Robotics and Autonomous Systems, vol. 55, no. 1, pp. 21–29, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. L. M. Paz, J. D. Tardós, and J. Neira, “Divide and conquer: EKF SLAM in O(n),” IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1107–1120, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. L. D'Alfonso, A. Grano, P. Muraca, and P. Pugliese, “Sensor fusion and surrounding environment mapping for a mobile robot using a mixed extended Kalman filter,” in Proceedings of the 10th IEEE International Conference on Control and Automation (ICCA '13), pp. 1520–1525, Hangzhou, China, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Montemerlo and S. Thrun, “Simultaneous localization and mapping with unknown data association using FastSLAM,” in Proceeding of the IEEE International Conference on Robotics and Automation, pp. 1985–1991, Karlsruhe, Germany, September 2003. View at Scopus
  24. F. Pei, M. Wu, and S. Zhang, “Distributed SLAM using improved particle filter for mobile robot localization,” The Scientific World Journal, vol. 2014, Article ID 239531, 10 pages, 2014. View at Publisher · View at Google Scholar
  25. D. Simon, “From here to infinity,” in Embedded Systems Programming, pp. 72–79, 2001. View at Google Scholar
  26. H. Ahmad and T. Namerikawa, “H filter convergence and its application to SLAM,” in Proceedings of the ICROS-SICE International Joint Conference, pp. 2875–2880, Fukuoka, Japan, August 2009.
  27. H. Ahmad and T. Namerikawa, “Feasibility study of partial observability in H filtering for robot localization and mapping problem,” in Proceedings of the American Control Conference (ACC '10), pp. 3980–3985, Baltimore, Md, USA, July 2010. View at Scopus
  28. H. Ahmad and T. Namerikawa, “Robot localization and mapping problem with unknown noise characteristics,” in Proceedings of the IEEE International Conference on Control Applications (CCA '10), pp. 1275–1280, Yokohama, Japan, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. H. Ahmad and T. Namerikawa, “Robotic mapping and localization considering unknown noise statistics,” Journal of System Design and Dynamics, vol. 5, no. 1, pp. 70–82, 2011. View at Google Scholar
  30. P. Bolzern, P. Colaneri, and G. De Nicolao, “H differential Riccati equations: convergence properties and finite escape phenomena,” IEEE Transactions on Automatic Control, vol. 42, no. 1, pp. 113–118, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. P. Bolzern and M. Maroni, “New conditions for the convergence of H filters and predictors,” IEEE Transactions on Automatic Control, vol. 44, no. 8, pp. 1564–1568, 1999. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. J. Andrade-Cetto and A. Sanfeliu, Environment Learning for Indoor Mobile Robots, Springer, 2006.
  33. T. Vidal-Calleja, J. Andrade-Cetto, and A. Sanfeliu, “Conditions for suboptimal filter stability in SLAM,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '04), pp. 27–32, Sendai, Japan, October 2004. View at Scopus