MARCO

As I've described before, the task of the vector transformation module is to ingest the results of fine acquisition and output an interpretive attitude reading.   The input to the transformation module is the x & y coordinates of each black dot in the center of each target.  This allows MARCO to generate the set of 6 unique vectors which I drew beautifully in the diagram below. As the more mathematically-inclined of you may know, in a perfect world with infinitely-precise centroid pixel coordinates I would need just 3 targets and two linearly-independent vectors, however this world is far from perfect and the additional target and vectors serve to greatly increase the accuracy of my readings. To save us all a great deal of time and brain-hurt, I will not talk in-depth about the mathematics that drive this transformation.  The actual equations boil down to relatively straightforward trigonometric operations and anyone who's taken college-level linear ...

All development related to target centroid finding is complete, with a few setbacks, the foremost of which being that Fine acq. translated nicely from a great heap of nested code into a nice warm dumpster fire.   I had to hedge my bets on a backup algorithm called black acq. which serves the same purpose, but requires a black dot in the center of the target where fine acq. could work with or without the dot (if it worked at all, which it doesn't). This marks the much-anticipated end of the computer vision/image processing phase of the project, which is nice.  The next step is vector transformation, which should be easy-peasy compared to the image processing portion.  I actually had most of that math worked out on paper with the intention of passing the coarse acq. results to it, but I could not narrow down the possible attitude past two distinct solution-spaces without more precise target centroid data. The green dots on each target are the results of coa...