Computation of shape through controlled active exploration

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Abstract
Accurate knowledge of depth continues to be of critical importance in robotic systems. Without accurate depth knowledge, tasks such as inspection, tracking, grasping, and collision-free motion planning prove to be difficult and often unattainable. Traditional visual depth recovery has relied upon techniques that require the solution of the correspondence problem or require known lighting conditions and Lambertian surfaces. In this paper, we present a technique for the derivation of depth from feature points on a target's surface using the controlled active vision framework. We use a single visual sensor mounted on the end-effector of a robotic manipulator to automatically select feature points and to derive depth estimates for those features using adaptive control techniques. Movements of the manipulator produce displacements that are measured using a sum-of-squared difference (SSD) optical flow. The measured displacements are fed into the controller to alter the path of the manipulator and to refine the depth estimate.

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