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Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments

By Michael Milford, David McKinnon, Michael Warren, Gordon Wyeth and Ben Upcroft

Abstract

In this paper we present a novel algorithm for localization during navigation that performs matching over local image sequences. Instead of calculating the single location most likely to correspond to a current visual scene, the approach finds candidate matching locations within every section (subroute) of all learned routes. Through this approach, we reduce the demands upon the image processing front-end, requiring it to only be able to correctly pick the best matching image from within a short local image sequence, rather than globally. We applied this algorithm to a challenging downhill mountain biking visual dataset where there was significant perceptual or environment change between repeated traverses of the environment, and compared performance to applying the feature-based algorithm FAB-MAP. The results demonstrate the potential for localization using visual sequences, even when there are no visual features that can be reliably detected

Topics: 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, Visual SLAM, stereo odometry
Publisher: Australian Robotics & Automation Association
Year: 2011
OAI identifier: oai:eprints.qut.edu.au:48011

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