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Dellaert, Frank

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    Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space
    (Georgia Institute of Technology, 2006-06) Quennesson, Kevin ; Dellaert, Frank
    Users know what they see before where they are: it is more natural to talk about high level visibility information ("I see such object") than about one's location or orientation. In this paper we introduce a method to find in 3D worlds a density of viewpoints of camera locations from high level visibility constraints on objects in this world. Our method is based on Rao-Blackwellized importance sampling. For efficiency purposes, the proposal distribution used for sampling is extracted from a visibility preprocessing technique adapted from computer graphics. We apply the method for finding in a 3D city model of Atlanta the virtual locations of real-world cameras and viewpoints.
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    On-line Learning of the Traversability of Unstructured Terrain for Outdoor Robot Navigation
    (Georgia Institute of Technology, 2006) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
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    A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping
    (Georgia Institute of Technology, 2005) Dellaert, Frank ; Krauthausen, Peter
    QR factorization is most often used as a black box algorithm, but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in the clique tree computation, we show that one straightforwardly obtains a method for performing inference in distributed sensor networks. One obvious application is distributed localization and mapping with a team of robots. We phrase the problem as inference on a large-scale Gaussian Markov Random Field induced by the measurement factor graph, and show how multifrontal QR on this graph solves for the global map and all the robot poses in a distributed fashion. The method is illustrated using both small and large-scale simulations, and validated in practice through actual robot experiments.