Scene signatures : localised and point-less features for localisation
McManus, Colin, Upcroft, Ben, & Newmann, Paul (2014) Scene signatures : localised and point-less features for localisation. In Robotics: Science and Systems X, 12-16 July 2014, University of California, Berkeley, CA.
This paper is about localising across extreme lighting and weather conditions. We depart from the traditional point-feature-based approach as matching under dramatic appearance changes is a brittle and hard thing. Point feature detectors are fixed and rigid procedures which pass over an image examining small, low-level structure such as corners or blobs. They apply the same criteria applied all images of all places. This paper takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. Our localisation task then turns into curating a large bank of spatially indexed detectors and we show that this yields vastly superior performance in terms of robustness in exchange for a reduced but tolerable metric precision. We present an unsupervised system that produces broad-region detectors for distinctive visual elements, called scene signatures, which can be associated across almost all appearance changes. We show, using 21km of data collected over a period of 3 months, that our system is capable of producing metric localisation estimates from night-to-day or summer-to-winter conditions.
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|Item Type:||Conference Paper|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science|
|Copyright Owner:||Copyright 2014 [please consult the author]|
|Deposited On:||15 Sep 2014 23:12|
|Last Modified:||16 Sep 2014 21:49|
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