Something I thought would be a simple problem turned out not to be - but mostly because of my own clumsyness. I just noticed a function in OpenCV that I could've used instead of spending time on programming it myself.
I found that through OpenCV, I can use the fundamental matrix - retrieved from at least seven point matches in two photos - and take the SVD (Singular Value Decomposition) on that as a step to find rotation and translation of points in the photos. Since I didn't at first find the SVD in OpenCV, I clumsily assumed that it wasn't in the library. After a day, I accidentaly came across it, but in what seems to be a slower version - cvmSVD() - instead of what I just found, the cvSVD() function :-)
I'll now find the fundamental matrix from point matches, use the internal camera parameters to normalize it into the Essential Matrix, and then use SVD to find the rotation and translation...
We'll see how it works - at least it gave me hope to be done in two weeks, like I had planned to from start :-)