CMPack: a complete software system for autonomous legged soccer robots
Proceedings of the fifth international conference on Autonomous agents
Multi-Fidelity Robotic Behaviors: Acting with Variable State Information
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Robust global localization using clustered particle filtering
Eighteenth national conference on Artificial intelligence
Active Appearance-Based Robot Localization Using Stereo Vision
Autonomous Robots
Robotics and Autonomous Systems
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
A Comparative Analysis of Particle Filter Based Localization Methods
RoboCup 2006: Robot Soccer World Cup X
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
RoboCup 2008: Robot Soccer World Cup XII
RoboCup 2008: Robot Soccer World Cup XII
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Localization with non-unique landmark observations
RoboCup 2010
IEEE Transactions on Robotics
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Successful approaches to the robot localization problem include particle filters, which estimate non-parametric localization belief distributions. Particle filters are successful at tracking a robot's pose, although they fare poorly at determining the robot's global pose. The global localization problem has been addressed for robots that sense unambiguous visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. Unfortunately, for robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new pose hypotheses across a wide region, in poses that may be inconsistent with previous observations. We introduce multi-observation sensor resetting (MOSR) to address the localization problem with sparse, ambiguous and noisy observations. MOSR merges observations from multiple frames to generate new hypotheses more effectively. We demonstrate experimentally on the NAO humanoid robots that MOSR converges more efficiently to the robot's true pose than standard sensor resetting, and is more robust to systematic vision errors.