  Controlling illumination can generate high quality information about object
surface normals and depth discontinuities at a low computational cost. In this
work we demonstrate a robot workspace-scaled controlled illumination approach
that generates high quality information for table top scale objects for robotic
manipulation. With our low angle of incidence directional illumination
approach, we can precisely capture surface normals and depth discontinuities of
monochromatic Lambertian objects. We show that this approach to shape
estimation is 1) valuable for general purpose grasping with a single point
vacuum gripper, 2) can measure the deformation of known objects, and 3) can
estimate pose of known objects and track unknown objects in the robot's
workspace.
