Gaurav Pandey

EE-698G: Probabilistic Mobile Robotics

Tentative Course Content

• Introduction to Probability Theory and Linear Algebra
• Robot Motion and Coordinate Frame Transformations
• Robot Vision: Lidars and Cameras
• Recursive State Estimation: Bayesian filter, Kalman filter (KF), EKF, UKF, Particle filter etc.
• Mobile Robot Mapping
• Mobile Robot Localization
• Simultaneous localization and Mapping (SLAM)
• Path Planning (A*, Randomly exploring random tree (RRT), Potential fields etc.)



Reference Books

• Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT press, 2005.



Meeting Time/Location

• Lecture: TBD
• Office Hours: Th 16:00 - 18:00, ACES Room #401A



Grading Policy

• TBD