Monte Carlo localization: efficient position estimation for mobile robots
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using Learning for Approximation in Stochastic Processes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Real-time hand tracking using a mean shift embedded particle filter
Pattern Recognition
Pedestrian localisation for indoor environments
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
Swarm-supported outdoor localization with sparse visual data
Robotics and Autonomous Systems
Multiswarm particle filter for vision based SLAM
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Adaptive parallel/serial sampling mechanisms for particle filtering in dynamic Bayesian networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Indoor Mobile Robotics at Grima, PUC
Journal of Intelligent and Robotic Systems
An adaptive sample count particle filter
Computer Vision and Image Understanding
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The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to reallocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the regular implementation of the filter. In practice this may be highly inappropriate since it ignores errors in the models and the varying dynamics of the processes. This work presents a self adaptive version of the particle filter that uses statistical methods to adapt the number of particles and the propagation function at each iteration. Furthermore, our method presents similar computational load than the standard particle filter. We show the advantages of the self adaptive filter by applying it to a synthetic example and to the visual tracking of targets in a real video sequence.