On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-cue Localization for Soccer Playing Humanoid Robots
RoboCup 2006: Robot Soccer World Cup X
Robust and Efficient Field Features Detection for Localization
RoboCup 2006: Robot Soccer World Cup X
Panoramic Localization in the 4-Legged League
RoboCup 2006: Robot Soccer World Cup X
Self-localization Using Odometry and Horizontal Bearings to Landmarks
RoboCup 2007: Robot Soccer World Cup XI
Constraint Based Belief Modeling
RoboCup 2008: Robot Soccer World Cup XII
IEEE Transactions on Signal Processing
Multi-observation sensor resetting localization with ambiguous landmarks
Autonomous Robots
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In the probabilistic robot localization problem, when the associations of observations with the landmarks in the map are given, the solution is straightforward. However, when the observations are non-unique (e.g. the association with the map is not given) the problem becomes more challenging. In the Standard Platform League (SPL) and other similar categories of RoboCup, as the field setups evolve over years, the observations become less informative. In the localization level, we have to seek solutions with non-unique landmark observations. In this paper, we established the probabilistic model of the problem and showed the difficulty of optimal solution. After that, we introduce our importance sampling based approximate solution and implicit hypothesis pruning. We give results from simulation tests in the SPL setup using corners and goal bar observations and discuss characteristics of our approach.