1. Principle of Bayesian
Robot Localization

2. 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:

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

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

5. Grid-based Markov Localization

6. Probability of a Laser Scan

7. Localization with Laser Range-finder