A fast, robust, open source startracker based on a new class of baysian startracker algorithms

Features:

• Fast lost in space identification
• Image to image matching
• 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)

sudo apt-get install python-scipy libopencv-dev python-opencv swig python-systemd

Additional packages needed for calibration and unit testing:

sudo apt-get install git astrometry.net python-astropy

cd /usr/share/astrometry

Download fits files corresponding to your camera fov size (see astrometry.net for details
sudo wget http://data.astrometry.net/4100/index-4112.fits
sudo wget http://data.astrometry.net/4100/index-4113.fits
sudo wget http://data.astrometry.net/4100/index-4114.fits
sudo wget http://data.astrometry.net/4100/index-4115.fits
sudo wget http://data.astrometry.net/4100/index-4116.fits
sudo wget http://data.astrometry.net/4100/index-4117.fits
sudo wget http://data.astrometry.net/4100/index-4118.fits
sudo wget http://data.astrometry.net/4100/index-4119.fits

git clone https://github.com/UBNanosatLab/openstartracker.git

cd openstartracker/tests
./unit_test.sh -crei xmas

cd openstartracker/
mkdir yourcamera
mkdir yourcamera/samples
mkdir yourcamera/calibration_data

add 3-10 star images of different parts of the sky taken with your camera to yourcamera/samples

edit APERTURE and EXPOSURE_TIME in calibrate.py (you want to take images with the lowest exposure time that consistently solves)

run ./unit_test.sh -crei yourcamera to recalibrate and test

The ESA test should have a score of >70. If its worse than this, play around with exposure time (50ms is a good starting point)