particlefilters.ppt

Probabilistic Robotics Importance Sampling with Resampling:Landmark Detection Example Distributions Distributions This is Easy! Importance Sampling with Resampling Importance Sampling with Resampling Particle Filter Algorithm Particle Filter Algorithm Resampling Given: Set S of weighted samples. Wanted : Random sample, where the probability of drawing xi is given by wi. Typically done n times with replacement to generate new sample set S’. Resampling Resampling Algorithm Motion Model Reminder Proximity Sensor Model Reminder Initial Distribution After Incorporating Ten Ultrasound Scans After Incorporating 65 Ultrasound Scans Estimated Path Using Ceiling Maps for Localization Vision-based Localization Under a Light Next to a Light Elsewhere Global Localization Using Vision Robots in Action: Albert Application: Rhino and Albert Synchronized in Munich and Bonn Localization for AIBO robots Limitations The approach described so far is able to track the pose of a mobile robot and to globally localize the robot. How can we deal with localization errors (i.e., the kidnapped robot problem)? Approaches Randomly insert samples (the robot can be teleported at any point in time). Insert random samples proportional to the average likelihood of the particles (the robot has been teleported with higher probability when the likelihood of its observations drops). Random SamplesVision-Based Localization 936 Images, 4MB, .6secs/image Trajectory of the robot: Odometry Information Image Sequence Resulting Trajectories Resulting Trajectories Global Localization Kidnapping the Robot Summary Particle filters are an implementation of recursive Bayesian filtering They represent the posterior by a set of weighted samples. In the context of localization, the particles are propagated according to the motion model. They are then weighted according to the likelihood of the observations. In a re-sampling step, new particles are drawn with a probability proportional to the likelihood of the o