Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-25T09:13:06.259Z Has data issue: false hasContentIssue false
  • English
  • Français

A comparison of Bayesian prediction techniques for mobile robot trajectory tracking1

Published online by Cambridge University Press:  01 September 2008

J. L. Peralta-Cabezas ,
M. Torres-Torriti  and
M. Guarini-Hermann
J. L. Peralta-Cabezas*
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
M. Torres-Torriti*
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
M. Guarini-Hermann
Affiliation:
Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Casilla 306-22, Santiago, Chile.
*
*Corresponding authors. E-mail: [email protected]; [email protected]
*Corresponding authors. E-mail: [email protected]; [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.

Keywords

Mobile robot trackingLatency compensationBayesian predictionKalman filteringMonte Carlo methodsSigma-Point filters
Type
Article
Information
Robotica , Volume 26 , Issue 5 , September 2008 , pp. 571 - 585
Copyright
Copyright © Cambridge University Press 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

1

This work was supported by Conycit of Chile under Fondecyt Grant 11060251.

References

1.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA, 2005).Google Scholar
2.Ristic, B., Arulampalam, S. and Gordon, N., Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House, Norwood, MA, 2004).Google Scholar
3. Special Issue on Sequential State Estimation, Proceedings of the IEEE 92(3), (2004).Google Scholar
4.Tsai, M.-C., Chen, K.-Y., Cheng, M.-Y. and Lin, K. C., “Implementation of a real-time moving object tracking system using visual servoing,” Robotica 21 (6), 615–625 (2003).Google Scholar
5.Hue, C., Le-Cadre, J.P. and Perez, P., “Sequential Monte Carlo methods for multiple target tracking and data fusion,” IEEE Trans. Signal Processing 50 (2), 309–325 (2002).Google Scholar
6.Orton, M. and Fitzgerald, W., “A Bayesian approach to tracking multiple targets using sensor arrays and particle filters,” IEEE Trans. Signal Processing 50 (2), 216–223 (2002).Google Scholar
7.Doucet, A., Godsill, S. and Andrieu, C., “On sequential Monte Carlo sampling methods for Bayesian filtering,” Stat. Comput. 10 (3), 197–208 (2000).Google Scholar
8.Bar-Shalom, Y. and Rong Li, X., Estimation and Tracking: Principles, Techniques, and Software (Ybs Publishing, Storrs, CT, USA, 1998).Google Scholar
9.Askary, F. and Sullivan, N. T., “Importance of measurement accuracy in statistical process control,” Proceedings of SPIE Metrology, Inspection, and Process Control for Microlithography XIV (Sullivan, N. T. ed.) 3998, Santa Clara, CA, USA (Feb. 28–Mar. 2, 2000) pp. 546–554.Google Scholar
10.van der Merwe, R., Wan, E. A. and Julier, S., “Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation”, Proceedings of the AIAA Guidance, Navigation and Control Conference (2004) pp. 5120.Google Scholar
11.Gloye, A., Simon, M., Egorova, A., Wiesel, F., Tenchio, O., Schreiber, M., Behnke, S. and Rojas, R., Predicting Away Robot Control Latency (Technical Report B-08-03, Freie Universität Berlin, Fachbereich Mathematik und Informatik, 2003).Google Scholar
12.Bouvet, D. and Garcia, G., “GPS latency identification by Kalman filtering,” Robotica 18 (5), 475–485 (2001).Google Scholar
13.Donoso-Aguirre, F., Bustos-Salas, J.-P., Torres-Torriti, M. and Guesalaga, A., “Mobile robot localization using the Hausdorff distance,” Robotica, to appear (published online by Cambridge University Press, 12 Jul 2007).Google Scholar
14.Anderson, B. D. O. and Moore, J. B., Optimal Filtering (Dover Publications, Mineola, NY, 2005).Google Scholar
15.Kalman, R. E., “New approach to linear filtering and prediction problems,” Trans. ASME 82, 34–45 (1960).Google Scholar
16.Jazwinski, A. H., Stochastic Processes and Filtering Theory (Academic Press, 1970).Google Scholar
17.Julier, S. J. and Uhlmann, J. K., “A new extension of the Kalman filter to nonlinear systems,” Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls, Orlando, Florida (1997) pp. 182–193.Google Scholar
18.Ito, K. and Xiong, K., “Gaussian filters for nonlinear filtering problems,” Proceedings of the IEEE International Conference on Robotics and Automation 45(5), San Francisco, CA, USA, (Apr. 24–28, 2000) pp. 910–927.Google Scholar
19.Nørgaard, M., Poulsen, N. K. and Ravn, O., “New developments in state estimation for nonlinear systems,” Automatica 36 (11), 1627–1638 (2000).Google Scholar
20.Van-der-Merwe, R. and Wan, E., “The Square-Root Unscented Kalman Filter for state and parameter estimation,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing 6, Salt Lake City, UT, USA, (May 7–11, 2001) pp. 3461–3464.Google Scholar
21.Van-der-Merwe, R., Doucet, A., de Freitas, N. and Wan, E., “The Unscented Particle Filter,” In:Advances in Neural Information Processing Systems (Leen, T. K., Dietterich, T. G. and Tresp, V. eds.), (MIT Press, Cambridge, MA, 2000) pp. 584–590.Google Scholar
22.Van-der-Merwe, R. and Wan, E., “Gaussian mixture Sigma-Point Particle Filters for sequential probabilistic inference in dynamic state-space models,” Proceedings of the International Conference on Acoustic, Speech and Signal Processing, Hong Kong, SAR China, (Apr. 6–10, 2003) pp. 701–704.Google Scholar
23.Kotecha, J. H. and Djuric, P. M., “Gaussian sum Particle Filtering for dynamic state space models,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 2001, Salt lake City, UT, USA, (May 7–11, 2001). pp. 3465–3468.Google Scholar
24.Cui, N., Hong, L. and Layne, J. R., “A comparison of nonlinear filtering approaches with an application to ground target tracking,” Signal Process., Elsevier 85 (8), 1469–1492 (2005).Google Scholar
25.Wright, R., Maskell, S. R. and Briers, M., “Comparison of Kalman-based methods with Particle Filters for raid tracking,” Pract. Bayesian Stat. 5 (2003).Google Scholar
26.Yuen, D. C. K. and MacDonald, B. A., “A comparison between Extended Kalman Filtering and Sequential Monte Carlo techniques for simultaneous localisation and map-building,” Proceedings of the 2002 Australasian Conference on Robotics and Automation, Auckland, Australia, (Nov. 27–29 2002) pp. 111–116.Google Scholar
27.Cuevas, E. V., Zaldívar, D. and Rojas, R., Kalman Filter for Vision Tracking (Technical Report B-05-12, Freie Universität Berlin, Fachbereich Mathematik und Informatik, 2005).Google Scholar
28.Cuevas, E. V., Zaldívar, D. and Rojas, R., Particle Filter for Vision Tracking (Technical Report B-05-13, Freie Universität Berlin, Fachbereich Mathematik und Informatik, 2005).Google Scholar
29.Metropolis, N. and Ulam, S., “The Monte Carlo method,” J. Am. Stat. Assoc. 44 (247), 335–341 (1949).Google Scholar
30.Gordon, N. J., Salmond, D. J. and Smith, A. F. M., “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” IEE Proc. F 140 (2), 107–113 (1993).Google Scholar
31.Goris, M. J., Gray, D. A. and Mareels, I. M. Y., “Reducing the computational load of a Kalman filter,” IEE Electron. Lett. 33 (18), 1539–1541 (1997).Google Scholar
32.Karlsson, R., Shön, T. and Gustafsson, F., “Complexity analysis of the marginalized particle filter,” IEEE Trans. Signal Process. 53 (11), 4408–4411 (2005).Google Scholar
33.Browning, B., Bowling, M. and Veloso, M., “Improbability filtering for rejecting false positives,” Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, USA, (May 11–15, 2002) pp. 3038–3043.Google Scholar
34.Liu, Y., Wu, X., Zhu, J. and Lew, J., “Omni-directional mobile robot controller design by trajectory linearization,” Proceedings of the 2003 American Control Conference 4, Denver, Co, USA, (June 4–6 2002) pp. 3423–3428.Google Scholar
35.Angeles, J., Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms, 3rd ed. (Springer, 2006).Google Scholar
36.van der Merwe, R., ReBEL: Recursive Estimation Bayesian Library (OGI School of Science & Engineering, Oregon Health & Science University (OHSU), 2002–2006), http://choosh.cse.ogi.edu/rebel/, visited on February 2006.Google Scholar
37.Nørgaard, M., Kalmtool: version 2, http://www.iau.dtu.dk/research/control/kalmtool.html Ole Ravn and Niels Kjølstad Poulsen, KalmTool: versions 3, 4, http://server.oersted.dtu.dk/www/or/kalmtool/, visited February 2006, (Department of Mathematical Modelling, Department of Automation, Ørsted - Danmarks Tekniske Universitet, 1998–2006).Google Scholar
38.Gadeyne, K., BFL: Bayesian Filtering Library (Department of Mechanical Engineering, Katholieke Universiteit Leuven, Belgium, 2001–2006), http://www.orocos.org/bfl, visited on February 2006.Google Scholar
39.Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, E. P., Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, MA, 1992).Google Scholar