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
CMPack: a complete software system for autonomous legged soccer robots
Proceedings of the fifth international conference on Autonomous agents
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
Multi-cue Localization for Soccer Playing Humanoid Robots
RoboCup 2006: Robot Soccer World Cup X
Proprioceptive Motion Modeling for Monte Carlo Localization
RoboCup 2006: Robot Soccer World Cup X
Planning for multi-robot localization
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
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This paper explores how sensor and motion modeling can be improved to better Markov localization by exploiting deviations from expected sensor readings. Proprioception is achieved by monitoring target and actual motions of robot joints. This provides information about whether or not an action was executed as desired, yielding a quality measure of the current odometry. Odometry is usually extremely prone to errors for legged robots, especially in dynamic environments where collisions are often unavoidable, due to the many degrees of freedom of the robot and the numerous possibilities of motion hindrance. A quality measure helps differentiate the periods of unhindered motion from periods where robot motion was impaired for whatever reason. Negative evidence is collected when a robot fails to detect a landmark that it expects to see. Therefore the gaze direction of the camera has to be modeled accordingly. This enables the robot to localize where it could not when only using landmarks. In the general localization task, the probability distribution converges more quickly when negative information is taken into account.