Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Williams, Henry, Browne, Will N, & Milford, Michael (2012) Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System. In Carnegie, Dale (Ed.) Proceedings of the 2012 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Wellington, New Zealand.

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Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

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ID Code: 57795
Item Type: Conference Paper
Refereed: Yes
Keywords: SLAM, Mapping, Supervised classifier system, SeqSLAM
ISBN: 978-0-9807404-3-1
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 (Please consult the authors).
Deposited On: 06 Mar 2013 05:04
Last Modified: 12 Sep 2016 01:29

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