Active camera systems can greatly increase flexibility of assembly
robots.  But incorporating active camera system into robot control
loop involves some difficulties such as calibration. In this project,
the goal is to implement a self-organizing neural network that can
learn a calibration-free spatial representation of 3D point targets
that is invariant to changing camera configuration. The representation
can be used in robot close-loop control.