R. [. Abidi and . Gonzalez, Data Fusion in Robotics and Machine Intelligence, 1992.

D. Arbuckle, A. Howard, and M. Matari´cmatari´c, Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment, IEEE/RSJ International Conference on Intelligent Robots and System, 2002.
DOI : 10.1109/IRDS.2002.1041424

R. [. Aji and . Mceliece, The generalized distributive law, IEEE Transactions on Information Theory, vol.46, issue.2, pp.325-343, 2000.
DOI : 10.1109/18.825794

. [. Arulampalam, . Maskell, T. Gordon, and . Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, 2002.
DOI : 10.1109/78.978374

N. [. Arras, R. Tomatis, and . Siegwart, Multisensor on-the-fly localization:, Robotics and Autonomous Systems, vol.34, issue.2-3, pp.131-143, 2001.
DOI : 10.1016/S0921-8890(00)00117-2

]. J. Bbc-+-95, W. Buhmann, A. B. Burgard, D. Cremers, T. Fox et al., The mobile robot Rhino, Artificial Intelligence Magazine, vol.16, issue.1, 1995.

. M. Bbfl, A. Bertozzi, A. Broggi, P. Fascioli, . D. Lombardibbs96-]-w et al., Vision-based pedestrian detection : will ants help ? Tracking maneuvering targets with multiple sensors : Does more data always mean better estimates ?, IEEE Transactions on Aerospace and Electronic Systems, vol.32, issue.1, 1996.

. Bdl-+-98a-]-p, E. Bessì-ere, O. Dedieu, E. Lebeltel, K. Mazer et al., Interprétation vs. description I : Proposition pour une théorie probabiliste des systèmes cognitifs sensori-moteurs. Intellectica, pp.26-27257, 1998.

. Bdl-+-98b-]-p, E. Bessì-ere, O. Dedieu, E. Lebeltel, K. Mazer et al., Interprétation vs. description II : Fondements mathématiques. Intellectica, pp.26-27313, 1998.

]. J. Bibliographie-[-bil98, . [. Bilmes, R. Blackman, . [. Popoli, T. E. Bar-shalom et al., A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models Design and Analysis of Modern Tracking Systems Artech House Tracking and Data Association Multitarget Multisensor Tracking : Principles and Techniques Tracking in a cluttered environment with probabilistic data association Tracking in a cluttered environment with probabilistic data association Chasing an elusive target with a mobile robot, Proceedings of the 4th Symposium on nonlinear estimation theory and its applicationsCB01] C. Coué and P.Bessì ere Proceedings of the IEEE-RSJ International Conference on Intelligent Robots and Systems, Hawai (HI), 1974.

X. Clady, F. Collange, F. Jurie, P. Martinetcoo90, and ]. T. Cox, Object tracking with a pan-tilt-zoom camera : application to car driving assistance [Col02] F.. Colas. Modélisation analytique et bayésienne des neurones d'orientation de la tête The computational complexity of probabilistic inference using bayesian belief network Probability, frequency and reasonable expectation The Algebra of Probable Inference, Proceedings of IEEE International Conference on Robotics and AutomationCro89] J. Crowley. World modeling and position estimation for a mobile robot using ultrasonic ranging Proc. of the IEEE Int. Conf. on Robotics and Automation, 1946.

A. Doucet, S. Godsill, and C. Andrieu, On sequential monte carlo sampling methods for bayesian filtering, Dia99] J. Diard. Apprentissage hiérarchique bayésien, 1999.

J. Diard, La carte bayséienne ? Un modèle probabiliste hiérarchique pour la navigation en robotique mobile, Thèse de doctorat, 2003.

N. [. Dempster, D. B. Laird, and . Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society, vol.39, pp.1-38, 1977.

]. A. Elf89 and . Elfes, Using occupancy grids for mobile robot perception and navigation, IEEE Computer, Special Issue on Autonomous Intelligent Machines, pp.46-57, 1989.

]. A. Elf92 and . Elfes, Multi-source spatial data fusion using bayesian theory, Data Fusion in Robotics and Machine Intelligence, 1992.

T. E. Fortmann, Y. Bar-shalom, and M. Scheffe, Sonar tracking of multiple targets using joint probabilistic data association, IEEE Journal of Oceanic Engineering, vol.8, issue.3, 1983.
DOI : 10.1109/JOE.1983.1145560

]. S. Fou03 and . Fournier, Détection de collision et d'´ echouage dans la plate-forme TRANS, GTAS'03 10èmes journées du groupe de travail Animation et Simulation, 2003.

]. D. Fox01 and . Fox, KLD-sampling : Adaptive particle filters, Advances in Neural Information Processing Systems 14, 2001.

]. J. Gew96 and . Geweke, Monte Carlo simulation and numerical integration, Handbook of Computational Economics, pp.731-800, 1996.

J. [. Gauvrit, C. Le-cadre, and . Jauffret, A formulation of multitarget tracking as an incomplete data problem, IEEE Transactions on Aerospace and Electronic Systems, vol.33, issue.4, 1997.
DOI : 10.1109/7.625121

]. N. Gor97 and . Gordon, A hybrid bootstrap filter for target tracking in clutter, IEEE Transactions on Aerospace and Electronic Systems, vol.33, issue.1, 1997.

Y. [. Houles and . Bar-shalom, Multisensor tracking of a manoeuvering target in clutter, IEEE Transactions on Aerospace and Electronic Systems, vol.25, 1989.

J. [. Hall and . Llinas, An introduction to multisensor data fusion, Proceedings of the IEEE, vol.85, issue.1, 1997.
DOI : 10.1109/5.554205

C. Hue, J. Cadre, and P. Pérez, Sequential Monte Carlo methods for multiple target tracking and data fusion, IEEE Transactions on Signal Processing, vol.50, issue.2, 2002.
DOI : 10.1109/78.978386

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.441

]. C. Hue03 and . Hue, Méthodes Séquentielles de Mont Carlo pour le filtrage non linéaire multi-objets dans un environnement bruité Applications au pistage multi-cibles Bibliographie etàetà la trajectographie d'entités dans des séquences d'images 2D, Janvier 2003. [Jay03] E. T. Jaynes. Probability Theory : the Logic of Science, 2003.

]. A. Jaz70 and . Jazwinsky, Stochastic Processes and Filtering Theory, 1970.

J. [. Julier, H. F. Uhlmann, and . Durrant-whyte, A new approach for filtering nonlinear systems, Proceedings of 1995 American Control Conference, ACC'95, 1995.
DOI : 10.1109/ACC.1995.529783

J. [. Julier, H. F. Uhlmann, and . Durrant-whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control, vol.45, issue.3, p.45, 2000.
DOI : 10.1109/9.847726

]. R. Kal60 and . Kalman, A new approach to linear filtering and prediction problems, Journal of basic Engineering, vol.35, 1960.

M. [. Kurien and . Liggins, Report-to-track assignement in multisensor multitarget tracking, 27th Conference on Decision and Control, 1988.

C. Koike, C. Pradalier, P. Bessì, and M. Mazer, Obstacle avoidance and proscriptive bayesian programming, Workshop on Reasonning with Uncertainty in Robotics, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2003.
DOI : 10.1109/iros.2003.1250660

URL : https://hal.archives-ouvertes.fr/hal-00019258

O. Lebeltel, P. Bessì-ere, J. Diard, and E. Mazer, Bayesian Robot Programming, Autonomous Robots, vol.16, issue.1, pp.49-79, 2004.
DOI : 10.1023/B:AURO.0000008671.38949.43

URL : https://hal.archives-ouvertes.fr/inria-00189723

Y. [. Li and . Bar-shalom, Tracking clutter with nearest neighbor filters : Analysis and performance, IEEE Transactions on Aerospace and Electronic Systems, vol.32, issue.3, 1996.

]. O. Leb99 and . Lebeltel, Programmation Bayésienne des Robots, Thèse de doctorat, 1999.

]. J. Mar47 and . Marcum, A statistical theory of targets detection by pulsed radar. Research Memorandum RM-745, The Rand Corporation, 1947.

L. [. Mittal and . Davis, M 2 Tracker : a multi-view approach to segmenting and tracking people in a cluttered scene, International Journal of Computer Vision, issue.3, p.51, 2003.

]. K. Mek99 and . Mekhnacha, Méthodes probabilistes baysiennes pour la prise en compte des incertitudes géométriques : applicationàapplicationà la CAO-robotique, Thèse de doctorat, Institut National Polytechnique de Grenoble (INPG), 1999.

R. [. Musicki, S. Evans, and . Stankovic, Integrated probabilistic data association, IEEE Transactions on Automatic Control, vol.39, issue.6, 1994.
DOI : 10.1109/9.293185

K. Mekhnacha, E. Mazer, and P. Bessì-ere, The design and implementation of a Bayesian CAD modeler for robotic applications, Advanced Robotics, vol.9, issue.4, pp.45-70, 2001.
DOI : 10.1163/156855301750095578

URL : https://hal.archives-ouvertes.fr/hal-00019182

G. Marsden, M. M. Donald, and M. Brackstone, Towards an understanding of adaptive cruise control, Transportation Research Part C: Emerging Technologies, vol.9, issue.1
DOI : 10.1016/S0968-090X(00)00022-X

]. H. Mor88 and . Moravec, Sensor fusion in certainty grids for mobile robots, AI Magazine, vol.9, issue.2, pp.61-74, 1988.

. [. Miller and E. R. Wagner, An Optical Rangefinder for Autonomous Robot Cart Navigation, Proc. of the 1987 SPIE/IECON, 1987.
DOI : 10.1007/978-1-4613-8997-2_11

]. R. Nea93 and . Neal, Probabilistic inference using Markov Chain Monte Carlo methods, 1993.

W. [. Orton and . Fitzgerald, A Bayesian approach to tracking multiple targets using sensor arrays and particle filters, IEEE Transactions on Signal Processing, vol.50, issue.2, 2002.
DOI : 10.1109/78.978377

S. [. Pao, Multisensor fusion algorithm for tracking, Proceedings of the American Control Conference, 1993.

]. R. Ras02, Apprentissage bayésien par imitation, 2002.

C. [. Stone, T. Barlow, and . Corwin, Bayesian Multiple Target Tracking, 1999.

D. Schulz, W. Burgard, D. Fox, and A. B. Cremers, Tracking multiple moving targets with a mobile robot using particle filters and statistical data association, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)
DOI : 10.1109/ROBOT.2001.932850

]. R. Sea71 and . Sea, An efficient suboptimal decision procedure for associating sensor data with stored tracks in real-time surveillance systems, Proceedings of the Conference on Decision and Control, 1971.

]. E. Sha93 and . Shahbazian, Implementation strategies for central-level multi-hypothesis tracking fusion with multiple dissimilar sensors, Signal Processing, Sensor Fusion, and Target Recognition II, Proc SPIE, pp.6-06, 1993.

]. R. Sin73, R. A. Singer, and . Sea, New results in oimizing surveillance system tracking and data correlation performance in dense multitarget environments, IEEE Transactions on Automatic Control, vol.18, pp.571-582, 1973.

T. [. Streit, Maximum likelihood methof for probabilistic multi-hypothesis tracking, SPIE International Symposium, Signal and Data Processing of Small Targets, 1994.

. L. Bibliographie-[-sl95-]-r and T. E. Streit, Probabilistic multi-hypothesis tracking, 1995.

]. B. Ste01 and . Steux, RT Maps, un environnement logiciel dédiédédiéà la conception d'applications embarquées temps-réel. Utilisation pour le détection automatique de véhicules par fusion radar/vision, Swe54] P. Swerling. Probability of detection for fluctuating targets. Research Memorandum RM-1217, 1954.

S. Thrun, M. Beetz, M. Bennewitz, W. Burgard, A. B. Cremers et al., Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva, The International Journal of Robotics Research, vol.19, issue.11, 2000.
DOI : 10.1177/02783640022067922

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.8565

]. S. Thr98 and . Thrun, Learning metric-topological maps for indoor mobile robot navigation, Artificial Intelligence, vol.99, issue.1, 1998.

]. S. Thr01 and . Thrun, Learning occupancy grids with forward models, Proc. of the IEEE-RSJ Int. Conf. on Intelligent Robots and Systems, Hawaii, HI (US), pp.oct-nov, 2001.

]. S. Thr02 and . Thrun, Robotic mapping : A survey, Exploring Artificial Intelligence in the New Millenium, 2002.

G. [. Welch and . Bishop, An introduction to the Kalman filter

G. R. Widmann, M. K. Daniels, L. Hamilton, L. Humm, B. Riley et al., Comparison of lidar-based and radarbased adaptive cruise control systems. SAE Technical paper series, 2000.

P. [. Yamauchi and . Langley, Place recognition in dynamic environments, Journal of Robotic System, vol.14, issue.2, 1997.