The first dense composite point cloud model has been generated from the GROK2 robot. Whilst the depth resolution might not be as good as a Kinect, and the registration of glimpses is not perfect, I think this proves - at least to my own satisfaction, if nobody else's - that stereo vision can be used as a practical depth sensing method.
There's still a fair amount of work to be done to improve on these results, but it's certainly looking feasible that recognition of sizable objects such as chairs or desk surfaces may be achievable. An obvious quick heuristic would simply be to run an elevation histogram and search for peaks which could indicate horizontally oriented surfaces.
No doubt the points could also be condensed into voxels to increase the efficiency of subsequent higher level processing.