This week, I focused on building and validating the core algorithmic pipeline for our celestial positioning system. I implemented star detection using preprocessing, adaptive thresholding, connected components, and centroid extraction on a stock starfield image. I then plate-solved the image using astrometry.net and integrated the resulting WCS file into our Python pipeline. The system achieved a mean angular error of approximately 0.0014 degrees, confirming that our detection and pose estimation steps are functioning accurately.
My progress is on schedule, as this week’s goal was to establish a working baseline registration and attitude estimation pipeline before implementing our own star identification method. Next week, I plan to replace the WCS-based labeling with a triangle-based star matching algorithm and validate that we can achieve similar accuracy independently.