People tracking with a mobile robot: A comparison of Kalman and particle filters

Proceedings of the 13th IASTED International Conference on Robotics and Applications, RA 2007 and Proceedings of the IASTED International Conference on Telematics

Volumn , Issue , 2007, Pages 388-393

  Bellotto, Nicola ^a   Hu, Osheng ^a  

^a UNIVERSITY OF ESSEX   (United Kingdom)

Author keywords

Kalman filter; Mobile robot; Multisensor fusion; Particle filter; People tracking

Indexed keywords

AIR FILTERS; APPLICATIONS; CELLULAR RADIO SYSTEMS; CONTROL THEORY; DISTRIBUTED COMPUTER SYSTEMS; EXTENDED KALMAN FILTERS; FUSION REACTIONS; KALMAN FILTERS; MOBILE ROBOTS; NONLINEAR FILTERING; ROBOTICS; SIGNAL FILTERING AND PREDICTION; TARGET TRACKING; WAVE FILTERS;

BAYESIAN ESTIMATORS; LOWER COMPUTATIONAL COSTS; MODERN SERVICES; MULTISENSOR FUSION; PARTICLE FILTER; PARTICLE FILTERS; PEOPLE TRACKING; SAMPLING IMPORTANCE RESAMPLING; SENSOR FUSIONS; UNSCENTED KALMAN FILTERS;

ROBOTS;

EID: 49349100806     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. W. Burgard, P. Trahanias, D. Hähnel, M. Moors, D. Schulz, H. Baltzakis, and Argyros A. TOURBOT and WebFAIR: Web-Operated Mobile Robots for Tele-Presence in Populated Exhibitions. In Proc. IROS 2002 Workshop on Robots in Exhibitions, 2002.
2. A. Treptow, G. Cielniak, and T. Duckett. Active people recognition using thermal and grey images on a mobile security robot. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2103.2108, Canada, 2005.
3. D. Beymer and K. Konolige. Tracking people from a mobile platform. In IJCAI-2001 Workshop on Reasoning with Uncertainty in Robotics, Seattle, WA, USA, 2001.
4. N. Bellotto and H. Hu. Vision and laser data fusion for track-ing people with a mobile robot. In Proc. IEEE Int. Conf. on Robotics and Biomimetics (ROBIO), pp. 7.12, China, 2006.
5. P. Chakravarty and R. Jarvis. Panoramic vision and laser range finder fusion for multiple person tracking. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 2949.2954, Beijing, China, 2006.
6. G. Welch and G. Bishop. An introduction to the kalman filter. Tech. Rep. 95-041, University of North Carolina, 2004.
7. S. J. Julier and J. K. Uhlmann. A New Extension of the Kalman Filter to Nonlinear Systems. In Proc. SPIE AeroSense Symposium, pp. 182.193, FL, USA, 1997.
8. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50 (2):174.188, 2002.
9. D. Schulz, W. Burgard, D. Fox, and A. B. Cremers. People tracking with mobile robots using sample-based joint probabilistic data association filters. Int. J. of Robotics Research, 22(2):99.116, 2003.
10. N. Bellotto and H. Hu. Multisensor based human tracking for an indoor service robot. Submitted to IJRR, 2007.
11. P. Viola and M. J. Jones. Robust real-time face detection. Int. J. of Computer Vision, 57(2):137.154, 2004.
12. Y. Bar-Shalom and X. R. Li. Multitarget-Multisensor Tracking: Principles and Techniques. Y. Bar-Shalom, 1995.
13. M. Bennewitz, G. Cielniak, and W. Burgard. Utilizing learned motion patterns to robustly track persons. In Proc. IEEE Int. W. on VS-PETS, pp. 102.109, France, 2003.
14. D. L. Hall and J. Llinas, editors. Handbook of multisensor data fusion. CRC Press, 2001.