Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Rao-blackwellised particle filtering for dynamic Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Cooperative Visual Tracking in a Team of Autonomous Mobile Robots
RoboCup 2006: Robot Soccer World Cup X
Proprioceptive Motion Modeling for Monte Carlo Localization
RoboCup 2006: Robot Soccer World Cup X
Sensor Modeling Using Visual Object Relation in Multi Robot Object Tracking
RoboCup 2006: Robot Soccer World Cup X
Integrating Simple Unreliable Perceptions for Accurate Robot Modeling in the Four-Legged League
RoboCup 2006: Robot Soccer World Cup X
Multi-robot Cooperative Localization through Collaborative Visual Object Tracking
RoboCup 2007: Robot Soccer World Cup XI
Cooperative Object Localization Using Line-Based Percept Communication
RoboCup 2007: Robot Soccer World Cup XI
Pure reactive behavior learning using Case Based Reasoning for a vision based 4-legged robot
Robotics and Autonomous Systems
Prioritized multihypothesis tracking by a robot with limited sensing
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
CooperativeWorld Modeling in Dynamic Multi-Robot Environments
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
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In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the non-linear parts of the state estimation problem. In the context of target tracking, these are the non-linear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a four-legged AIBO robot and tested in the context of ball tracking in the RoboCup domain.