An award-winning, open source startracker based on a new class of Bayesian startracker algorithms
Introduction
OpenStarstacker is a project to make a robust, high performance startracker freely available under the MIT license. It was originally developed in support of the University at Buffalo Nanosatellite Lab's GLADOS and SORA nanosatellite missions in order to fill their need for a high performance startracker software package.
Features:- Fast lost in space identification
- Image to image matching (relative tracking)
- Collect and store size, shape and color information of unknown objects
- Tracks unknown objects between images
- Programable python frontend / reusable C++ backend (BEAST-2) with no external dependencies
- Uses astrometry.net for calibration (check if your camera is good enough by uploading your star images to nova.astrometry.net)
Download
OpenStarstacker can be obtained from GitHub
People
Andrew Tennenbaum - Lead Developer
- questions welcome (but consider using the forum if the answer would be of use to others)
John L. Crassidis - Faculty Advisor
Publications
Automatic Star-tracker Optimization Framework - accepted as a finalist in the student paper competition in the 2017 Small Satellite Conference in Logan, Utah.
Contribute
If you or your organization is able to contribute to the development of OpenStartracker, please don't hesitate to get in contact with us. We also welcome contributions on GitHub