Welcome to CSE 571,
This course will introduce various techniques for probabilistic state
estimation and discuss their application to problems such as robot
localization, mapping, and manipulation. The course will also provide
a problem-oriented introduction to relevant machine learning and
computer vision techniques.
The class meets TTh 12:00-1:20 in SAV (Savery Hall) 132.
- Overview of mobile robotics (hardware, software architectures, sensors)
- Probabilistic models of sensing and acting
- Bayesian state estimation and filtering: Kalman filters
(extended, unscented), particle filters, dynamic Bayesian networks
- Robot localization
- Map building / SLAM
- Kinect cameras for mapping, modeling, and recognition
- Markov decision processes (MDPs, POMDPs, reinforcement learning)
- Additional estimation and control topics (manipulation, object
recognition and modeling, ...)
- Probabilistic Robotics.
S. Thrun, W. Burgard, and D. Fox.
MIT Press, Cambridge, MA, September 2005.