MPG - Fast Forward Reasoning on 6 DOF Pose Uncertainty

Estimation of the pose of an object, or equivalently estimation of a rigid motion, is a prerequisite to many tasks in robotics like perception and manipulation. We want to estimate object poses, consisting of orientation and position of a target object, with 6 degrees of freedom (DOF). We use dual quaternions for the representation of the pose and Mixtures of Projected Gaussians as probability density function over all possible object poses. The framework can deal with widely spread density functions and provides closed form calculations of their fusion. Further the framework allows for the compositions of motion. In this paper we present two improvements of our framework: an explicit treatment of uncertainties due to approximations and a fast integration over the 6-dimensional space of poses. Finally the improved framework is demonstrated using different types of features in combination.