Performance evaluation and metrics for perception in intelligent manufacturing

Abstract

An unsolved but important problem in intelligent manufacturing is dynamic pose estimation under complex environmental conditions - tracking an object's pose and position as it moves in an environment with uncontrolled lighting and background. This is a central task in robotic perception, and a robust, highly accurate solution would be of use in a number of manufacturing applications. To be commercially feasible, a solution must also be benchmarked against performance standards so manufacturers fully understand its nature and capabilities. The PerMIS 2008 Special Session on "Performance Metrics for Perception in Intelligent Manufacturing," held August 20, 2008, brought together academic, industrial and governmental researchers interested in calibrating and benchmarking vision and metrology systems. The special session had a series of speakers who each addressed a component of the general problem of benchmarking complex perception tasks, including dynamic pose estimation. The components included assembly line motion analysis, camera calibration, laser tracker calibration, super-resolution range data enhancement and evaluation, and evaluation of 6DOF pose estimation for visual servoing. This Chapter combines and summarizes the results of the special session, giving a framework for benchmarking perception systems and relating the individual components to the general framework. © 2009 Springer-Verlag US.