A probabilistic measurement model for local interest point based 6 DOF pose estimation

The ability to recognize objects and to localize them precisely is essential in all service robotic applications. One of the main challenges for service robots during operation lies in the handling of unavoidable uncertainties which originate from model and sensor inaccuracies and are characteristic for realistic application scenarios. Robustness under real world conditions can only be achieved when the dominant uncertainties are explicitly represented and purposefully managed by the robot´s control system. We therefore adopt a probabilistic approach in which environment perception over time is regarded as a sequential estimation process and follow a Bayesian filtering methodology. Under these assumptions probabilistic models of the robot´s perception systems play a decisive role. In this paper we describe our object localization system which is based on local features and uses 3D models that are created in an off-line modeling process. A probabilistic model of the errors, which occur in the 6D localization based on local features, is directly derived from the pose reconstruction procedure. Experimental results from an household scenario illustrate the effectiveness of our approach.