SIFT,SURF&Seasons:Appearance-basedLong-termLocalization inOutdoorEnvironments
Achim J. Lilienthal · 2009
AbstractIn this paper, we address the problem of outdoor, appearance-based topological localization, particularly over longperiods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forwardmethod that relies on local image features to compare single image pairs. We rst look into which of the dominatingimage feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then ne-tune ourlocalization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result.The nal localization algorithm is applied on multiple data sets, each consisting of a large number of panoramicimages, which have been acquired over a period of nine months with large seasonal changes. The nal localizationrate in the single-image matching, cross-seasonal case is between 80 to 95%. Key words: Localization, Scene Recognition, Outdoor Environments 1. IntroductionLocal feature matching has become an increas-ingly used method for comparing images. Variousmethods have been proposed. The Scale-InvariantFeature Transform (SIFT) by Lowe [14] has, withits high accuracy and relatively low computationtime, become the de facto standard. Some attemptsof further improvements to the algorithm havebeen made (for example PCA-SIFT by Ke andSukthankar [10]). Perhaps the most recent, promis-ing approach is the Speeded Up Robust Features(SURF) by Bay et al. [5], which has been shown to