Bayesian Estimation for Flight Robotics (Lecture + Practical)
Format: 24 Lectures (12 weeks) + 24 Practical Sessions
Occurrence: Offered every summer semester
Course Level: Master Students (Typically in their 1st or 2nd semester)
State estimation in autonomous UAVs, or flying robots, is a critical functionality. It allows a UAV to accurately localize itself and estimate the positions of a tracked target (from the UAV). Without robust state estimation, any of the other functionalities like planning, decision making and even low-level control can be jeopardized. The goal of this course is to provide students with a deep understanding of Bayesian estimation techniques and how they can be applied for UAV state estimation and target tracking. Students should be able to appreciate parametric ad non-parametric forms of estimation and their pros and cons. Finally, they should be able to implement various Bayesian filters for online applications, both in simulation and in a real UAV.