Principle of Bayesian Robot Localization.

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Presentation transcript:

Principle of Bayesian Robot Localization

Robot Localization as State Estimation (1) Lt: position of the robot at time t Given: Map and sensor model: Motion model: Initial state of the robot: Data Sensor information (sonar, laser range-finder, camera) oi Odometry information ai Wanted:

Motion Model Translational and rotational error are normally distributed represented by independent distributions

Robot Localization as State Estimation (2) Markov! Markov! Motion: Perception: … is optimal under the Markov assumption Kalman filters, Hidden Markov Models, DBN

Grid-based Markov Localization

Probability of a Laser Scan

Localization with Laser Range-finder