Probabilistic Robotics

Course content

Introduction to probabilistic methods for mobile robots and autonomous systems. Recursive state and parameter estimation using Bayes filter. Probabilistic robot motion, actuator and motion models. Probabilistic robot sensing and perception, sensor models. Gaussian and non-parametric filters for estimation. Canonical problems of localization and mapping. Simultaneous localization and mapping (SLAM). Introduction to probabilistic planning and control.