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

Milford, Michael, McKinnon, David, Warren, Michael, Wyeth, Gordon, & Upcroft, Ben (2011) Feature-based visual odometry and featureless place recognition for SLAM in 2.5D environments. In Drummond, Tom (Ed.) ACRA 2011 Proceedings, Australian Robotics & Automation Association, Monash University, Melbourne, VIC, pp. 1-8.

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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.

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ID Code: 48011
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Visual SLAM, stereo odometry
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Institute for Creative Industries and Innovation
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2011 Australian Robotics & Automation Association
Deposited On: 11 Jan 2012 22:56
Last Modified: 09 Feb 2015 11:37

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