Recovering structure uncertainties from noisy sense data
This work examines closely the possibilities for errors, mistakes and uncertainties in sensing systems. We identify and suggest techniques for modeling, analyzing, and recovering these uncertainties. This work concentrates on uncertainties in visual sensing for manipulators. The goal is to recover three-dimensional structure and motion characteristics of the environments under observation from noisy measurements. We also conjecture that the approaches described here are suitable for other sensors and parameters to be recovered. The computed uncertainties are utilized for reconstructing the geometry, motion parameters, and structure parameters under observation.
sensing computer vision structure from vision noisy sensory data recovering depth parameters from vision