The current plan is to attempt to construct a 2D map based upon the features from omnidirectional stereo vision. I can locate edge features close to the ground plane quite well, but the trouble with edges is that they're not very unique. I could use the edge data, projected into cartesian coordinates, to begin building a local map, but after a short time the map would begin to degenerate.
So what's needed are more unique features rather than edges. These could be tracked between frames (data association), and I could then use an off-the-shelf graph based SLAM algorithm, such as TORO to build a map. At first I thought of using SIFT, which would be the obvious choice if I were an academic researcher, but there are software patent issues associated with that method that I'd rather not have to deal with. FAST corners would be nice, but the relatively low resolution caused by the mirror distortion means that this algorithm doesn't work well. But I can use the Harris corner features from "good features to track" which is already built into OpenCV. Having been an OpenCV refusenick for quite a number of years I'm now slowly growing to like it. Harris corners seem to work quite reliably, despite the low resolution.