Lab Meeting December 22th, 2008 (slyfox):σSLAM:Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and Novel Mixture Proposal Distribution

Title: σSLAM: Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and a Novel Mixture Proposal Distribution

Author: Pantelis Elinas, Robert Sim, James J. Little

Abstract:
We consider the problem of Simultaneous Localization and Mapping (SLAM) using the Rao-Blackwellised Particle Filter (RBPF) for the class of indoor mobile robots equipped only with stereo vision. Our goal is to construct dense metric maps of natural 3D point landmarks for large cyclic environments in the absence of accurate landmark position measurements and motion estimates. Our work differs from other approaches because landmark estimates are derived from stereo vision and motion estimates are based on sparse optical ow. We distinguish between landmarks using the Scale Invariant Feature Transform (SIFT). This is in contrast to current popular approaches that rely on reliable motion models derived from odometric hardware and
accurate landmark measurements obtained with laser sensors. Since our approach depends on a particle filter whose main component is the proposal distribution, we develop and evaluate
a novel mixture proposal distribution that allows us to robustly close large loops. We validate our approach experimentally for long camera trajectories processing thousands of images at
reasonable frame rates.

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