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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 which 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 perception is regarded as a sequential estimation process and follow a Bayesian filtering methodology. Under these assumptions probabilistic models of the robot's perception systems are key.In this paper we shortly describe a model based object recognition and localization system. However, we do not not focus on the 6D pose estimation procedure itself, but on the method to quantify and compute the uncertainty associated with it. We construct a Gaussian approximation of the resulting pose error using the implicit function theorem. It is then used as a proposal density for importance sampling. Our goal is to sample from the measurement model describing 6D object localization based on local features in a Bayesian filtering context.

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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.