Pose estimation in industrial machine vision systems under sensing dynamics: A statistical learning approach

This paper deals with the problem of pose estimation (i.e., estimating position and orientation of an moving target) for real-time visual servoing, where the vision hardware is assumed to have severely limited measurement capability. In other words, we aim to compensate the slow sensor dynamics in industrial machine vision systems. The common approach is to predict the present target motion by propagating the delayed estimates with the target dynamics. Such method is sometimes problematic since the target motion characteristics (i.e., target dynamics) may change from one visual servoing task to another. Therefore, this paper presents a method which is able to estimate the target pose as well as learn the target dynamics. We apply the Expectation-Maximization algorithm to simultaneously solve the pose estimation problem and the target dynamics modeling problem. Several techniques including the extended Kalman filter/smoother, the block coordinate descent method, and the convex optimization method are utilized to address this problem. The effectiveness of the proposed algorithm is demonstrated experimentally on a 6-DOF industrial robot.