Scene Signatures: Localised and Point-less Features for Localisation
Colin McManus, Ben Upcroft, Paul Newmann · 2014
This paper is about localising across extreme lighting and weather conditions.We depart from the traditional point-feature-based approach since matching under dramatic appearance changes is a brittle and hard.Point-feature detectors are rigid procedures which pass over an image examining small, low-level structure such as corners or blobs.They apply the same criteria to 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 21 km of data collected over a period of 3 months, that our system is capable of producing metric estimates from night-to-day or summer-to-winter conditions.