Read my Medium series here: https://medium.com/building-autonomous-flight-software
This repo contains all the videos and learning material for the Autonomous Flight course, the most valuable part is the course notes that I wrote summarizing the content in each video.
Videos and exercises for every lecture are in the corresponding lesson folders
- 03 - Control.md -> Dropbox Paper
- 04 - Estimation - 01 - Intro to Estimation.md -> Dropbox Paper
- 04 - Estimation - 02 - Intro to Sensors.md -> Dropbox Paper
- 04 - Estimation - 03 - Extended Kalman Fil.md -> Dropbox Paper
- 04 - Estimation - 04 - 3D EKF and UKF.md -> Dropbox Paper
- 3D Motion Planning and Demo video on youtube
- C++ Flight Controller
- Sensor Fusion for State Estimation uses complimentary filters and an extended kalman filter and explains all the mathematics behind them.
- Bitcraze crazyflie EKF implementation
- Estimation_for_Quadrotors_.pdf - formulas for implementing EKF for autonomous flight sensors
- Quadcopter Dynamics, Simulation and Control : https://andrew.gibiansky.com/blog/physics/quadcopter-dynamics/
- Kalman filter 55 video series
- https://medium.com/@rymshasiddiqui/path-planning-using-potential-field-algorithm-a30ad12bdb08
- https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
- https://forum.unity.com/threads/ai-influence-maps.145368/
- https://gamedevelopment.tutsplus.com/tutorials/understanding-goal-based-vector-field-pathfinding--gamedev-9007
- The Unscented Kalman Filter for Nonlinear Estimation is a short paper (only 6 pages) and does a great job explaining the UKF.
- A New Extension of the Kalman Filter to Nonlinear Systems is a bit longer (12 pages) and includes some helpful example applications.
- The Scaled Unscented Transformation gives a mathematical derivation of the unscented transform.
- Fusing Inertial Sensor Data in an Extended Kalman Filter for 3D Camera Tracking