The MVP sensor planning system for robotic vision tasks

The MVP model-based sensor planning system for robotic vision is presented. MVP automatically synthesizes desirable camera views of a scene based on geometric models of the environment, optical models of the vision sensors, and models of the task to be achieved. The generic task of feature detectability has been chosen since it is applicable to many robot-controlled vision systems. For such a task, features of interest in the environment are required to simultaneously be visible, inside the field of view, in focus, and magnified as required. In companion papers we analytically characterize the domain of admissible camera locations, orientations, and optical settings for which each of the above feature detectability requirements is satisfied separately. In this paper, we present a technique that poses the vision sensor planning problem in an optimization setting and determines viewpoints that satisfy all previous requirements simultaneously and with a margin. In addition, we present experimental results of this technique when applied to a robotic vision system that consists of a camera mounted on a robot manipulator in a hand-eye configuration. The camera is positioned and the lens is focused according to the results generated by MVP. Camera views taken from the computed viewpoints verify that all feature detectability constraints are indeed satisfied.