/AutonomousPrecisionLanding

Precision landing on a visual target using OpenCV and dronekit-python

Primary LanguagePythonMIT LicenseMIT

AutonomousPrecisionLanding

Autonomously landing a drone on a visual target. This project was built to prototype an algorithm for the Mohammad Bin Zayed International Robotics Competition 2017.

The project makes use of the dronekit-python library to connect to a Pixhawk based drone. Dronekit-sitl is used to validate the algorithm in a simulated environment. The target tracking is performed using the Viola Jones approach for Rapid Object Detection using HAAR-cascades.

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Install Dependencies

  1. Install Python 2.7 here.
  2. Install OpenCV-python 3.1.0 from here.
  3. Install Dronekit from here.

How To Run

Create a directory called Vids/ in Logs/

  1. Run the code with python main.py to run on simulation.
  2. Run the code with python main.py --connect <connection_string> to run on the drone.

Contributors

  1. Nikhil Venkatesh
  2. Rahul Nambiar

Acknowledgements

  1. Daniel Nugent's work on precision landing - https://github.com/djnugent/Precland.
  2. Viola and Jones' paper - Rapid Object Detection using a Boosted Cascade of Simple Features.

Disclaimer - This repository is no longer maintained.