Theoretical Considerations of Multiple Particle Filters for Simultaneous Localisation and Map-Building

The rationale of adopting multiple particle filters to solve the Simultaneous Localisation and Map-building (SLAM) problem is discussed in this paper. SLAM can be considered as a combined state and parameter estimation problem. The particle filtering based solution is not only more flexible than the established extended Kalman filtering method, but also offers computational advantages. Experimental results based on a standard SLAM data set verify the feasibility of the method.

[1]  Patric Jensfelt,et al.  Active global localization for a mobile robot using multiple hypothesis tracking , 2001, IEEE Trans. Robotics Autom..

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

[3]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[4]  Peter Corke,et al.  Experimental Robotics VI, The Sixth International Symposium, Sydney, Australia, March 26-28, 1999 , 2000, ISER.

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

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

[7]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[8]  Sebastian Thrun,et al.  Particle Filters in Robotics , 2002, UAI.

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

[10]  Randall Smith,et al.  Estimating Uncertain Spatial Relationships in Robotics , 1987, Autonomous Robot Vehicles.

[11]  Hugh F. Durrant-Whyte,et al.  An Experimental and Theoretical Investigation into Simultaneous Localisation and Map Building , 1999, ISER.

[12]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[13]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[14]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.