MVP sensor planning and modeling system for machine vision

Abstract

In this paper we present an overview of the MVP sensor planning and modeling system that we have developed. MVP automatically determines camera viewpoints and settings so that object features of interest are simultaneously visible, inside the field-of-view, in-focus and magnified as required. We have analytically characterized the domain of admissible camera locations, orientations and optical settings for each of the above feature detectability requirements. In addition, we have posed the problem in an optimization setting in order to determine viewpoints that simultaneously satisfy all previous requirements. The location, orientation and optical settings of the computer viewpoint are achieved in the employed sensor setup by using sensor calibration models. For this purpose, calibration techniques have been developed that determine the mapping between the parameters that are planned and the parameters that can be controlled in a sensor setup which consists of a camera in a hand-eye arrangement equipped with a lens that has zoom, focus and aperture control. Experimental results are shown of these techniques when all the above feature detectability constraints are included. In order to verify satisfaction of these constraints, camera views are taken from the computed viewpoints by a robot vision system that is positioned and its lens is set according to the results of this method.