One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual range of the robot. The approach has been implemented and tested on a real robot using laser-range data. We present experiments illustrating that our algorithm is able to robustly keep track of multiple people. The experiments furthermore show that the approach outperforms other techniques developed so far.
Get full access to this article
View all access options for this article.
References
1.
Aggarwal, J. K., and Cai, Q. 1999. Human motion analysis: A review . Computer Vision and Image Understanding73(3): 428-440 .
2.
Arras, K. O., and Vestli, S. J. 1998. Hybrid, high-precision localization for the mail distributing mobile robot system MOPS . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
3.
Asoh, H., Hayamizu, S., Hara, I., Motomura, Y., Akaho, S., and Matsui, 1997. T. Socially embedded learning of office-conversant robot jijo-2 . In Proceedings of IJCAI-97. IJCAI, Inc.
4.
Bar-Shalom, Y., and Fortmann, T. E. 1988. Tracking and Data Association. Mathematics in Science and Engineering. Academic Press, New York .
5.
Bennewitz, M., Burgard, W., and Thrun, S. 2002. Learning motion patterns of persons for mobile service robots . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
6.
Beymer, D., and Konolige, K. 2001. Tracking people from a mobile platform . In IJCAI-2001 Workshop on Reasoning with Uncertainty in Robotics.
7.
Black, M. J., and Jepson, A. D. 1998. A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions. In ECCV.
8.
Bui, H., Venkatesh, S., and West, G. 2001. Tracking and surveillance in wide-area spatial environments using the Abstract Hidden Markov Model . International Journal of Pattern Recognition and Artificial Intelligence.
9.
Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., and Thrun, S. 1999. Experiences with an interactive museum tour-guide robot . Artificial Intelligence114(1-2).
10.
Cox, I. J., Miller, M. L., Danchick, R., and Newnam, G. E. 1997. A comparison of two algorithms for determining ranked assignments with application to multi-target tracking and motion correspondence . IEEE Transactions on Aerospace and Electronic Systems33(1): 295-301 .
11.
Cox, I. J. 1993. A review of statistical data association techniques for motion correspondence . International Journal of Computer Vision10(1): 53-66 .
12.
Cox, I. J., and Hingorani, S. L. 1996. An efficient implementation of Reids multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking . IEEE Transactions on Pattern Analysis and Machine Intelligence18(2): 138-150 .
13.
Darrell, T., Moghaddam, B., and Pentland, A. P. 1996. Active face tracking and pose estimation in an interactive room . In Proc. IEEE 6th Int. Conf. on Computer Vision. pp. 67-72 .
14.
Dellaert, F., Fox, D., Burgard, W., and Thrun, S. 1999. Monte Carlo localization for mobile robots . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
15.
Doucet, A., de Freitas, N., and Gordon, N., editors. 2001. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York .
16.
Endres, H., Feiten, W., and Lawitzky, G. 1998. Field test of a navigation system: Autonomous cleaning in supermarkets . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
17.
Engelberger, J. F. 1993. Health-care robotics goes commercial: The “helpmate” experience . Robotica11: 517-523 .
18.
Fod, A., Howard, A., and Matarić, M. J. 2002. Laser-based people tracking . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
19.
Fox, D. 2002. KLD-Sampling: Adaptive particle filters . In Advances in Neural Information Processing Systems 14 (NIPS).
20.
Fox, D., Burgard, W., Dellaert, F., and Thrun, S. 1999. Monte Carlo localization: Efficient position estimation for mobile robots . In Proc. National Conf. on Artificial Intelligence (AAAI).
21.
Fox, D., Thrun, S., Dellaert, F., and Burgard, W. 2001. Particle filters for mobile robot localization. In Doucet, A., de Freitas, N., and Gordon, N., editors. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York .
22.
González-Baños, H.H., Lee, C. Y., and Latombe, J. C. 2002. Real-time combinatorial tracking of a target moving unpredictably among obstacles . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
23.
Gordon, N.J. 1997. A hybrid bootstrap filter for target tracking in clutter . IEEE Transactions on Aerospace and Electronic Systems33(1): 353-358 .
24.
Gordon, N. J., Salmond, D. J., and Smith, A. F. M. 1993. A novel approach to non-linear/non-Gaussian Bayesian state estimation . IEE Proceedings F140(2): 107-113 .
25.
Gross, H.-M., Boehme, H.-J., and König, A. 2002. Vision-based Monte Carlo self-localization for a mobile service robot acting as shopping assistant in a home store . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
26.
Gutmann, J.-S., Burgard, W., Fox, D., and Konolige, K. 1998. An experimental comparison of localization methods . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
27.
Hähnel, D., Schulz, D., and Burgard, W. 2002. Map building with mobile robots in populated environments . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
28.
Horswill, I. 1993. Polly: A vision-based artificial agent . In Proc. National Conf. on Artificial Intelligence (AAAI).
29.
Illmann, J., Kluge, B., and Prassler, E. 2002. Statistical recognition of motion patterns . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
30.
Isard, M., and Blake, A. 1996. Contour tracking by stochastic propagation of conditional density . In Proc. European Conf. of Computer Vision.
31.
Jung, B., and Sukhatme, G. S. 2002. A region-based approach for cooperative multi-target tracking in a structural environment . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
32.
Kahn, R. E., Swain, M.J., Prokopowicz, P. N., and Firby, R. J. 1996. Gesture recognition using the perseus architecture. Technical Report TR-96-04, University of Chicago. p. 19.
33.
Kanazawa, K., Koller, D., and Russell, S. J. 1995. Stochastic simulation algorithms for dynamic probabilistic networks . In Proc. 11th Annual Conf. on Uncertainty in AI (UAI), Montreal, Canada.
34.
Kluge, B., Koehler, C., and Prassler, E. 2001. Fast and robust tracking of multiple moving objects with a laser range finder . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
35.
Koller-Meier, E. and Ade, F. 2001. Tracking multiple objects using the condensation algorithm . Journal of Robotics and Autonomous Systems34(2-3): 93-105 .
36.
Kortenkamp, D., Huber, E., and Bonasso, R. P. 1996. Recognizing and interpreting gestures on a mobile robot . In Proc. American Conf. on Artificial Intelligence.
37.
Kruse, E., and Wahl, F. 1998. Camera-based monitoring system for mobile robot guidance . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
38.
Lavalle, S. M., González-Baños, H. H., Becker, G., and Latombe, J.-C. 1997. Motion strategies for maintaining visibility of a moving target . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
39.
Lenser, S., and Veloso, M. 2000. Sensor resetting localization for poorly modelled mobile robots . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
40.
Lin, X., Kirubarajan, T., Bar-Shalom, Y., and Maskell, S. 2002. Comparison of EKF, pseudomeasurement filter, and particle filter for a bearing-only target tracking problem . In Proceedings of SPIE 4728 .
41.
Lindström, M. and Eklundh, J.-O. 2001. Detecting and tracking moving objects from a mobile platform using a laser range scanner . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
42.
Lu, F., and Milios, E. E. 1994. Robot pose estimation in unknown environments by matching 2D range scans . In IEEE Computer Vision and Pattern Recognition Conference (CVPR).
43.
MacCormick, J., and Blake, A. 1999. A probabilistic exclusion principle for tracking multiple objects . In Proc. 7th Int. Conf. on Computer Vision (ICCV). pp. 572-587 .
44.
Montemerlo, M., Thun, S., Koller, D., and Wegbreit, B. 2002. FastSLAM: A factored solution to simultaneous mapping and localization . In Proc. National Conf. on Artificial Intelligence (AAAI).
45.
Montemerlo, M., Thun, S., and Whittaker, W. 2002. Conditional particle filters for simultaneous mobile robot localization and people-tracking . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
46.
Moravec, H. P., and Elfes, A. E. 1985. High resolution maps from wide angle sonar . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA). pp. 116-121 .
47.
Murphy, K., and Russell, S. 2001. Rao-Blackwellised particle filtering for dynamic Bayesian networks. In Doucet, A., de Freitas, N., and Gordon, N., editors. Sequential Monte Carlo Methods in Practice. Springer Verlag, New York .
48.
Murrieta-Cid, R., González-Baños, H. H., and Tovar, B. 2002. A reactive motion planner to maintain visibility of unpredictable targets . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
49.
Parker, L. E. 1997. Cooperative motion control for multi-target observation . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
50.
Pirjanian, P., and Matarić, M. 2000. Multi-robot target acquisition using multiple objective behaviour coordination . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
51.
Pitt, M. K., and Shephard, N. 1999. Filtering via simulation: auxiliary particle filters . Journal of the American Statistical Association94(446).
52.
Rasmussen, C., and Hager, G. D. 1998. Joint probabilistic techniques for tracking multi-part objects . In Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR).
53.
Simmons, R., Goodwin, R., Haigh, K., Koenig, S., and O'Sullivan, J. 1997. A layered architecture for office delivery robots . In Proc. 1st Int. Conf. on Autonomous Agents, Marina del Rey, CA.
54.
Tadokoro, S., Hayashi, M., Manabe, Y., Nakami, Y., and Takamori, T. 1995. On motion planning of mobile robots which coexist and cooperate with humans . In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS).
55.
Thrun, S., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F., Fox, D., Hähnel, D., Rosenberg, C., Roy, N., Schulte, J., and Schulz, D. 1999. MINERVA: A second generation mobile tour-guide robot . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
56.
Vlassis, N., Terwijn, B., and Kröse, B. 2002. Auxiliary particle filter robot localization from high-dimensional sensor observations . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
57.
Waldherr, S., Thrun, S., Romero, R., and Margaritis, D. 1998. Template-based recognition of pose and motion gestures on a mobile robot . In Proc. National Conf. on Artificial Intelligence (AAAI).
58.
Wolf, J., Burgard, W., and Burkhardt, H. 2002. Robust vision-based localization for mobile robots using an image retrieval system based on invariant features . In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA).
59.
Wren, C. R., Azarbayejani, A., Darrell, T., and Pentland, A. P. 1997. Pfinder: Real-time tracking of the human body . IEEE Transactions on Pattern Analysis and Machine Intelligence19(7): 780-785 .
60.
Zhu, Q. 1991. Hidden Markov model for dynamic obstacle avoidance of mobile robot navigation . IEEE Transactions on Robotics and Automation7(3).
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
