Extended Kalman filter-based mobile robot localization with intermittent measurements

Systems Science and Control Engineering

Volumn 1, Issue 1, 2013, Pages 113-126

  Ahmad, Hamzah ^a   Namerikawa, Toru ^b  

^a UNIVERSITY MALAYSIA PAHANG   (Malaysia)

^b KEIO UNIVERSITY   (Japan)

Author keywords

Estimation; Extended Kalman filter (EKF); Intermittent measurements; Robot localization

Indexed keywords

BANDPASS FILTERS; ESTIMATION; EXTENDED KALMAN FILTERS; MOBILE ROBOTS; ROBOT APPLICATIONS;

ANALYSIS OF MEASUREMENTS; ESTIMATION PERFORMANCE; ESTIMATION RESULTS; INNOVATION CHARACTERISTICS; INTERMITTENT MEASUREMENTS; JACOBIAN TRANSFORMATION; MOBILE ROBOT LOCALIZATION; ROBOT LOCALIZATION;

UNCERTAINTY ANALYSIS;

EID: 84899421769     PISSN: None     EISSN: 21642583     Source Type: Journal    
DOI: 10.1080/21642583.2013.864249     Document Type: Article

References (26)

1. Ahmad, H., & Namerikawa, T. (2010a). Feasibility study of partial observability in H infinity filtering for robot localization and mapping problem. American control conference, Baltimore, USA, pp. 3980–3985.
2. Ahmad, H., & Namerikawa, T. (2010b). Robot localization and mapping problem with unknown noise characteristics. Multi-conference on systems and control, Yokohama, Japan, pp. 1275–1280. doi:10.1109/CCA.2010.5611272
3. Ahmad, H., & Namerikawa, T. (2011a). Intermittent measurement in robotic localization and mapping with FIM statistical bound. IEEJ Transaction on Electronics, Information and Systems, 131, 1223–1232. Retrieved from https://www.jstage.jst.go.jp/article/ieejeiss/131/6/131_6_1223/_article
4. Ahmad, H., & Namerikawa, T. (2011b). Robotic mapping and localization considering unknown noise statistics. Journal of System Design and Dynamics, 5, 70–82. doi:10.1299/jsdd.5.70
5. Censi, A. (2008). On the performance of Kalman filtering with intermittent observations: A geometric approach with fractals. American control conference, Washington, DC, pp. 3806–3812. doi:10.1109/ACC.2009.5159869
6. Chen, H., Sun, D., Yang, J., & Chen, J. (2009). Localization for multirobot formations in indoor environment. IEEE/ASME Transactions on Mechatronics, 15, 561–574. doi:10.1109/TMECH.2009.2030584
7. Csorba, M., & Durrant-Whyte, H. F. (1996). A new approach to simultaneous localisation and map building. Proceedings of the SPIE Aerosense, Orlando, USA, pp. 278–284.
8. Dissanayake, M.W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transaction on Robotics and Automation, 17, 229–241. doi:10.1109/70.938381
9. Huang, S., & Dissayanake, G. (2007). Convergence and consistency analysis for extended Kalman filter based SLAM. IEEE Transaction on Robotics, 23, 1036–1049. doi:10.1109/TRO.2007.903811
10. ∞ filtering for mobile robot localization using wireless sensor network. IEEE Sensors Journal, 13, 245–252. doi:10.1109/JSEN.2012.2213337
11. Jun, H., Zidong, W., Huijun, G., & Lampros, S. K. (2012). Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements. Automatica, 48, 2007–2015. doi:10.1016/j.automatica.2012.03.027
12. Kluge, S., Reif, K., & Brokate, M. (2010). Stochastic stability of the extended Kalman filter with intermittent observations. IEEE Transaction on Automatic Control, 55, 514–518. doi:10.1109/TAC.2009.2037467
13. Lynch, P. M., & Figueroa, J. F. (1991). Position estimation with intermittent measurements. American control conference, Arlington, USA, pp. 2280–2285.
14. Martinelli, A., & Siegwart, R. (2005). Observability analysis for mobile robot localization. IEEE/RSJ international conference on intelligent robots and systems, Alberta, USA, pp. 1471–1476.
15. Mo, Y., & Sinopoli, B. (2008). A characterization of the critical value for Kalman filtering with intermittent observations. IEEE conference of decision and control, Cancun, Mexico, pp. 2692–2697. doi:10.1109/CDC.2008.4739119
16. Muraca, P., Pugliese, P., & Rocc, G. (2008). An extended Kalman filter for the state estimation of a mobile robot from intermittent measurements. 16th Mediterranean conference on control and automation, Ajaccio-Corsica, France, pp. 1850–1855. doi:10.1109/MED.2008.4602251.
17. Nahi, N. E. (2002). Optimal recursice estimation with uncertain observation. IEEE Transactions on Information Theory, 15, 457–462. doi:10.1109/TIT.1969.1054329.
18. Payeur, P. (2008). Dealing with uncertain measurements in virtual representations for robot guidance. IEEE International Symposium on Virtual and Intelligent Measurement Systems, Istanbul, Turkey, pp. 2151–2158. doi:10.1109/VIMS.2002.1009357.
19. Plarre, K., & Bullo, F. (2009). On Kalman filtering for detectable systems with intermittent measurements. IEEE Transactions on Automatic Control, 54, 386–390. doi:10.1109/TAC.2008.2008347.
20. Se, S., Lowe, D. G., & Little, J. J. (2001). Local and global localization for mobile robots using visual landmarks. IEEE/RSJ international conference on intelligent robots and systems, Maui, USA, Vol. 1, pp. 414–420. doi:10.1109/IROS.2001.973392.
21. Se, S., Lowe, D. G., & Little, J. J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transaction on Robotics, 21(3), 364–375. doi:10.1109/TRO.2004.839228.
22. Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M. I., & Sastry, S. S. (2004). Kalman filtering wth intermittent observations. IEEE Transactions on Automatic Control, 49, 1453–1464. doi:10.1109/TAC.2004.834121.
23. Stephen, S., Lowe, D., & Little, J. (2002). Global localization using distinctive visual features. IEEE/RSJ international conference on intelligent robots and system, Lausanne, Switzerland, Vol. 1, pp. 226–231. doi:10.1109/IRDS.2002.1041393.
24. Tsai, C. C. (1998). A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurements. IEEE Transaction on Instrumentation and Measurement, 47, 1399–1404. doi:10.1109/19.746618.
25. Vidal-Calleja, T., Andrade-Cetto, J., & Sanfeliu, A. (2004). Conditions for suboptimal filter stability in SLAM. IEEE/RSJ international conference on intelligent robots and systems, Sendai, Japan, pp. 771–777. doi:10.1109/IROS.2004.1389324.
26. ∞ filtering with missing measurements and quantization effects. IEEE Transactions on Automatic Control, 58, 1707–1718. doi:10.1109/TAC.2013.2241492