Robotics: Science and Systems X

Scene Signatures: Localised and Point-less Features for Localisation

Colin McManus, Ben Upcroft, Paul Newmann


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.



    AUTHOR    = {Colin McManus AND Ben Upcroft AND Paul Newmann}, 
    TITLE     = {Scene Signatures: Localised and Point-less Features for Localisation}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2014}, 
    ADDRESS   = {Berkeley, USA}, 
    MONTH     = {July},
    DOI       = {10.15607/RSS.2014.X.023}