eigen.h, eigen.hpp come from PCL (just there for reference). Will be used to
transform on (x,y,z) coordinate system into another orthogonal coordinate system.

I have computed a collection of ortho vectors along with various dot products.
Check out processPlanes() in segment_clouds.cpp .

With these, I will rotate each to a system (1,0,0), (0,1,0), (0,0,1) . The first
camera will have a transformation  T1, and the second camera a transformation T2.
The composite transformation will thus be inv(T2)*T1(camera1 vector)  to transform
points from camera 1 to the coordinate system of camera 2. One will still need a translation
to position the clouds correctly. (Height should be easy. z might be possible (since
both cameras are in the same room, pointing towards the end of the room.). One could also assume
that the object of interest is approximately at the same distance from each camera.
I now compute the transformation of the cloud to a standard coordinate  (1,0,0), (0,1,0), (0,0,1)
Seems to work. However, registration cannot be tested (from a rotational point of view), until I do
the analysis for two clouds.

Note: It is soon time to reintroduce initial voxel filtering. The easiest way will be to filter every other
point out of the original organized cloud, before removing the NaNs.  That will accelerate algorithms by
at least a factor of 4.