Particle Filters; Simultaneous Localization and Mapping ...

Particle Filters; Simultaneous Localization and Mapping (Intelligent Autonomous Robotics)Subramanian RamamoorthySchool of InformaticsRecap: State Estimation using KalmanFilter Project state and error covariance forward in time: Update estimate after measurement:10 March 2009PF & , during a time interval, youexpect the ball to go from 1mto m,with some uncertainty increase In fact, vision sees ball going to mso you update your estimates toConclude ball must be at mwith some new level of uncertaintyRecap: State Estimation using KalmanFilter Project state and error covariance forward in time: Update estimate after measurement:10 March 2009PF & SLAM3 Limitations of the KalmanFilter Optimal state estimator for Linearsystems & Gaussiannoise Most robots involve nonlinear dynamics (simple example: stick-slip friction and slippage in tyres) Many commonly used sensors, , sonar, involve more complex type of noise In complex scenarios ( , estimating positions of obstacles in a room), one is dealing with multi-modal distributions Standard extensions for nonlinearity may not be satisfactory: If initial state estimate is wrong, or if process is incorrectly modeled, the filter could quickly diverge Covariance is underestimated10 March 2009PF & S

H. Durrant-Whyte and T. Bailey, Simultaneous localization and mapping, IEEE Robotics and ... Many pictures and equations in this presentation are taken from Giorgio Grisetti and Cyrill Stachniss, ECMR 2007 Tutorial 10 March 2009 PF & SLAM 29. Title: Planning and Control: Introduction (Intelligent Autonomous Robotics) ...

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