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Mark A. Paskin

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UAI Conference 2004 Conference Paper

Robust Probabilistic Inference in Distributed Systems

Mark A. Paskin
Carlos Guestrin

Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a natural fit for distributed systems, but they must be robust to the failure situations that arise in real-world settings, such as unreliable communication and node failures. Unfortunately, the popular sum�product algorithm can yield very poor estimates in these settings because the nodes' beliefs before convergence can be arbitrarily different from the correct posteriors. In this paper, we present a new message passing algorithm for probabilistic inference which provides several crucial guarantees that the standard sum�product algorithm does not. Not only does it converge to the correct posteriors, but it is also guaranteed to yield a principled approximation at any point before convergence. In addition, the computational complexity of the message passing updates depends only upon the model, and is dependent of the network topology of the distributed system. We demonstrate the approach with detailed experimental results on a distributed sensor calibration task using data from an actual sensor network deployment.

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IJCAI Conference 2003 Conference Paper

Thin Junction Tree Filters for Simultaneous Localization and Mapping

Mark A. Paskin

Simultaneous Localization and Mapping ( S L A M ) is a fundamental problem in mobile robotics: while a robot navigates in an unknown environment, it must incrementally build a map of its surroundings and, at the same time, localize itself within that map. One popular solution is to treat SLAM as an estimation problem and apply the Kalman filter; this approach is elegant, but it does not scale well: the size of the belief state and the time complexity of the filter update both grow quadratically in the number of landmarks in the map. This paper presents a filtering technique that maintains a tractable approximation of the belief state as a thin junction tree. The junction tree grows under filter updates and is periodically "thinned" via efficient maximum likelihood projections so inference remains tractable. When applied to the SLAM problem, these thin junction tree filters have a linearspace belief state and a linear-time filtering operation. Further approximation yields a filtering operation that is often constant-time. Experiments on a suite of SLAM problems validate the approach.

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