Robert Eidenberger

Active perception and scene modeling by planning with probabilistic 6D object poses

This paper presents an approach to probabilistic active perception planning for scene modeling in cluttered and realistic environments. When dealing with complex, multi-object scenes with arbitrary object positions, the estimation of 6D poses including their expected uncertainties is essential. The scene model keeps track of the probabilistic object hypotheses over several sequencing sensing actions to represent the real object constellation.

Local dependency analysis in probabilistic scene estimation

A general solution to the problem of jointly estimating the state of multiple entities is regarded computationally challenging at the time. Most solutions are based on the application of wide assumptions of independence. In many situations and constellations of entities, this is sufficient and leads to high quality results. In some situations as occlusion for instance the assumption of independence is violated heavily resulting in considerable errors. The proposed approach considers local dependencies, allowing to increase the accuracy of the estimation punctually, depending on application requirements, such as high precision localization for grasping operations or rough precision for semantic localization.

Scene Analysis for Service Robots

A scene analysis module for service robots is presented which uses SIFT in a stereo setting, a systematic handling of uncertainties and an active perception component. The system is integrated and evaluated on the DESIRE two-arm mobile robot. Complex everyday scenes composed of various items from a 100-object database are analyzed successfully and efficiently.

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 robots 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 robots 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.