Aerial Robotics
( Robotics )

Lecture 1 : Introduction

Lecture 2: Transformations

Lecture 3: Rigid Body Motion

Lecture 4 : Rigid Body Dynamics

Lecture 5 : Aerodynamics and Propulsion

Lecture 6 : Control Introduction

Lecture 7 : Control Theory

Lecture 8 : Control: LTI Systems

Lecture 9 : Model Predictive Control

Lecture 10 : Sensors

Lecture 11: State Estimation: Bayesian Estimation - Kalman Filter

Lecture 12: State Estimation: Attitude & Heading - Extended Kalman Filter

Lecture 13: State Estimation: SLAM Extended Kalman Filter

Lecture 14: State Estimation: SLAM Unscented Kalman Filter, Particle Filter

Lecture 15: State Estimation: SLAM Graph Optimization

Lecture 16: State Estimation: Quaternion EKF & SE(3) Optimization

Lecture 17: L1 Guidance

Lecture 18: Planning: Sampling-based, PRM, sPRM, PRM*, RRT

Lecture 19: Planning: Sampling-based, RRG, RRT*

Lecture X: Practical Aerial Robotics
by: Stephen J. Carlson