Dellaert, Frank

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Now showing 1 - 2 of 2
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    Automatic Landmark Detection for Topological Mapping Using Bayesian Surprise
    (Georgia Institute of Technology, 2008) Ranganathan, Ananth ; Dellaert, Frank
    Topological maps are graphical representations of the environment consisting of nodes that denote landmarks, and edges that represent the connectivity between the landmarks. Automatic detection of landmarks, usually special places in the environment such as gateways, in a general, sensor-independent manner has proven to be a difficult task. We present a landmark detection scheme based on the notion of “surprise” that addresses these issues. The surprise associated with a measurement is defined as the change in the current model upon updating it using the measurement. We demonstrate that surprise is large when sudden changes in the environment occur, and hence, is a good indicator of landmarks. We evaluate our landmark detector using appearance and laser measurements both qualitatively and quantitatively. Part of this evaluation is performed in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
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    Segmental Switching Linear Dynamic Systems
    (Georgia Institute of Technology, 2005) Oh, Sang Min ; Rehg, James M. ; Dellaert, Frank
    We introduce Segmental Switching Linear Dynamic Systems (S-SLDS), which improve on standard SLDSs by explicitly incorporating duration modeling capabilities. We show that S-SLDSs can adopt arbitrary finite-sized duration models that describe data more accurately than the geometric distributions induced by standard SLDSs. We also show that we can convert an S-SLDS to an equivalent standard SLDS with sparse structure in the resulting transition matrix. This insight makes it possible to adopt existing inference and learning algorithms for the standard SLDS models to the S-SLDS framework. As a consequence, the more powerful S-SLDS model can be adopted with only modest additional effort in most cases where an SLDS model can be applied. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed S-SLDS model.