Particle Filters in Robotics (Invited Talk)

This presentation will introduce the audience to a new, emerging body of research on sequential Monte Carlo techniques in robotics. In recent years, particle filters have solved several hard perceptual robotic problems. Early successes were limited to low-dimensional problems, such as the problem of robot localization in environments with known maps. More recently, researchers have begun exploiting structural properties of robotic domains that have led to successful particle filter applications in spaces with as many as 100,000 dimensions. The presentation will discuss specific tricks necessary to make these techniques work in real - world domains,and also discuss open challenges for researchers IN the UAI community.

[1]  Xavier Boyen,et al.  Tractable Inference for Complex Stochastic Processes , 1998, UAI.

[2]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[3]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1992 .

[4]  Eduardo Mario Nebot,et al.  Optimization of the simultaneous localization and map-building algorithm for real-time implementation , 2001, IEEE Trans. Robotics Autom..

[5]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[6]  Kevin P. Murphy,et al.  Bayesian Map Learning in Dynamic Environments , 1999, NIPS.

[7]  Raja Chatila,et al.  An Experimental System for Incremental Environment Modelling by an Autonomous Mobile Robot , 1989, ISER.

[8]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[9]  Liqiang Feng,et al.  Navigating Mobile Robots: Systems and Techniques , 1996 .

[10]  Manuela M. Veloso,et al.  Sensor resetting localization for poorly modelled mobile robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Mark S. Boddy,et al.  An Analysis of Time-Dependent Planning , 1988, AAAI.

[12]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[13]  William Whittaker,et al.  Conditional particle filters for simultaneous mobile robot localization and people-tracking , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[14]  Stefan B. Williams,et al.  Towards terrain-aided navigation for underwater robotics , 2001, Adv. Robotics.

[15]  Sebastian Thrun,et al.  Coastal Navigation with Mobile Robots , 1999, NIPS.

[16]  Hugh F. Durrant-Whyte,et al.  A Bayesian Algorithm for Simultaneous Localisation and Map Building , 2001, ISRR.

[17]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[18]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[19]  John J. Leonard,et al.  A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization , 2000 .

[20]  Sebastian Thrun,et al.  Monte Carlo POMDPs , 1999, NIPS.

[21]  Rodney A. Brooks,et al.  A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network , 1989, Neural Computation.

[22]  Sebastian Thrun,et al.  A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Javier Nicolás Sánchez,et al.  Robust global localization using clustered particle filtering , 2002, AAAI/IAAI.

[24]  Wolfram Burgard,et al.  Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva , 2000, Int. J. Robotics Res..

[25]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[26]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[27]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[28]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[29]  Wolfram Burgard,et al.  Tracking multiple moving targets with a mobile robot using particle filters and statistical data association , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[30]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[31]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .