Active Stereo Reconstruction
Active stereo systems are composed of two cameras with computer controlled vergence,
pan, and tilt, which resemble the human vision system. The calibration of active stereo
vision systems, needed for traditional model based 3D reconstruction, requires the
calibration of two cameras and their kinematics. To avoid the difficult calibration
process we explored the use of neural networks to reconstruct the 3D positions of objects
from information collected by the stereo head. We were able to successfully demonstrate
that our artificial neural network was a good alternative to traditional model based
methods. In order to compare our neural network to traditional model based reconstruction, a
method to calibrate the active stereo system was needed. We found that methods presented in
literature were sensitive to error and produced undesirable results. Building on these
reported techniques we created a new method for calibration that was robust to errors in
the calibration data. Visually Modulated Motion
Current visual servoing systems are intended to provide slow iterative motions and
thus are not capable of performing tasks that require quick and complex movements.
Visually modulated motion (VMM) was created to address this limitation. The VMM system
maps visual input directly to a set of motor commands that generate a complex and fast
motion that is relatively short in duration. Once the motion is performed the outcome is
analyzed and the mapping between the visual input and the motor commands is updated.
This biologically inspired paradigm of learning from previous motions gives the VMM
system the ability to achieve the desired outcome with sufficient repetition.