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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.

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