Combined extended FIR/Kalman filtering for indoor robot localization via triangulation

Measurement: Journal of the International Measurement Confederation

Volumn 50, Issue 1, 2014, Pages 236-243

  Pomárico Franquiz, Juan ^a   Khan, Sanowar H ^b   Shmaliy, Yuriy S ^a  

^a UNIVERSITY OF GUANAJUATO   (Mexico)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

Extended FIR filtering; Extended Kalman filtering; Robot localization; Triangulation

Indexed keywords

ALGORITHMS; EXTENDED KALMAN FILTERS; IMPULSE RESPONSE; ITERATIVE METHODS; MOBILE ROBOTS; TRIANGULATION;

COMBINED ALGORITHMS; EXTENDED FIR FILTERING; EXTENDED KALMAN FILTERING; FINITE-IMPULSE RESPONSE; KALMAN FILTERING ALGORITHMS; MOBILE ROBOT LOCALIZATION; OPERATION CONDITIONS; ROBOT LOCALIZATION;

ROBOT APPLICATIONS;

EID: 84893510192     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2013.12.045     Document Type: Article

References (30)

1. J. Borenstein, H.R. Everett, L. Feng, S.W. Lee, and R.H. Byrne Where am I? Sensors and Methods for Mobile Robot Positioning 1996 University of Michigan
2. A. Nüchter 3D Robotic Mapping: The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom 2009 Springer-Verlag Berlin
3. H.F. Durrant-Whyte Integration, Coordination and Control of Multi-Sensor Robot Systems 1987 Kluwer New York
4. J.J. Leonard, and H.F. Durrant-Whyte Mobile robot localization by tracking geometric beacons IEEE Trans. Rob. Autom. 7 3 1991 376 382
5. L. Kleeman, Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning, in: Proc. IEEE Int. Conf. Robot. and Autom., Nice, France, 1992, pp. 2582-2587. (Pubitemid 23615264)
6. S. Shoval, and D. Sinriech Analysis of landmarks configuration for absolute positioning of autonomous vehicles J. Manuf. Syst. 20 1 2001 44 54 (Pubitemid 33613102)
7. L. Cheng, C.-D. Wu, and Y.-Z. Zhang Indoor robot localization based on wireless sensor networks IEEE Trans. Consum. Electron. 57 3 2011 1099 1104
8. I. Amundson, J. Sallai, X. Koutsoukos, and Á. Lédeczi RF angle of arrival-based node localization Int. J. Sens. Networks 9 3/4 2011 209 224
9. K. Sugihara Some location problems for robot navigation problems using a single camera Comput. Vision, Graphics, Image Process. 42 1 1988 112 129 (Pubitemid 18591637)
10. E. Aitenbitchler, M. Mühlhäuser, An IR local positioning system for smart items and devices, in: Proc. 23rd Int. Conf. Distrib. Comput. Syst. Workshops, 2003, pp. 334-339.
11. E. Martín-Gorostiza, F.J. Meca, J.L. Lázaro-Galilea, E. Martos-Naya, F.B. Naranjo, and Ó. Esteban Coverage-mapping method based on a hardware model for mobile-robot positioning in intelligent spaces IEEE Trans. Instrum. Meas. 59 2 2010 266 282
12. Z. Wang, and D. Gu Cooperative target tracking control of multiple robots IEEE Trans. Ind. Electron. 59 8 2012 3232 3240
13. S. Han, H. Lim, and J. Lee An efficient localization scheme for a differential-drive mobile robot based on RFID system IEEE Trans. Ind. Electron. 54 6 2007 3362 3369
14. H. Cox On the estimation of state variables and parameters for noisy dynamic systems IEEE Trans. Autom. Control AC 9 1964 5 12
15. H. Cho, and S.W. Kim Mobile robot localization using biased chirp-spread-spectrum ranging IEEE Trans. Ind. Electron. 57 8 2010 2826 2835
16. Y. Motai, and A. Kosaka Hand-eye calibration applied to viewpoint selection for robotic vision IEEE Trans. Ind. Electron. 55 10 2008 3731 3741
17. Y. Bar-Shalom, X.-R. Li, and T. Kirubarajan Estimation with Applications to Tracking and Navigation 2001 J. Wiley & Sons New York
18. S.Y. Chen Kalman filter for robot vision: a survey IEEE Trans. Ind. Electron. 59 11 2012 4409 4420
19. R.J. Fitzgerald Divergence of the Kalman filter IEEE Trans. Autom. Control AC 16 6 1971 736 747
20. B. Gibbs Advanced Kalman Filtering, Least-Squares and Modeling 2011 Wiley New York
21. S.J. Julier, J.K. Uhlmann, A new extension of the Kalman filter to nonlinear systems, in: Proc. SPIE, vol. 3068, 1997, pp. 182-193.
22. F. Martinerie, P. Forster, Data association and tracking using hidden Markov models and dynamic programming, in: Proc. IEEE Int. Conf. Acoust. Speech Signal Process, (ICASSP-92), San Francisco, CA, 1992, pp. 449-452.
23. L.R. Rabiner, and B.H. Juang An introduction to hidden Markov models IEEE Acoust. Speech Signal Process. Mag. 3 1 1986 4 16
24. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans. Signal Process. 50 2 2002 174 188 (Pubitemid 34291500)
25. D. Schulz, W. Burgard, D. Fox, A.B. Cremers, Tracking multiple moving targets with a mobile robot using particle filters and statistical data association, in: IEEE Int. Conf. on Robotics Autom. (ICRA), vol. 2, 2001, pp. 1665-1670. (Pubitemid 32815097)
26. Y.S. Shmaliy Suboptimal FIR filtering of nonlinear models in additive white Gaussian noise IEEE Trans. Signal Process. 60 10 2012 5519 5527
27. W.H. Kwon, and S. Han Receding Horizon Control: Model Predictive Control for State Models 2005 Springer London
28. Y.S. Shmaliy Linear optimal FIR estimation of discrete time-invariant state-space models IEEE Trans. Signal Process. 58 6 2010 3086 3096
29. F. Ramirez-Echeverria, A. Sarr, and Y.S. Shmaliy Optimal memory for discrete-time FIR filters in state space IEEE Trans. Signal Process. 62 3 2014 557 561
30. Y.S. Shmaliy An iterative Kalman-like algorithm ignoring noise and initial conditions IEEE Trans. Signal Process. 59 6 2011 2465 2473