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Image features for retrieval-based localization must be invariant to dynamicobjects (e.g. cars) as well as seasonal and daytime changes. Such invariancesare, up to some extent, learnable with existing methods using triplet-likelosses, given a large number of diverse training images. However, due to thehigh algorithmic training complexity, there exists insufficient comparisonbetween different loss functions on large datasets. In this paper, we train andevaluate several localization methods on three different benchmark datasets,including Oxford RobotCar with over one million images. This large scaleevaluation yields valuable insights into the generalizability and performanceof retrieval-based localization. Based on our findings, we develop a novelmethod for learning more accurate and better generalizing localizationfeatures. It consists of two main contributions: (i) a feature volume-basedloss function, and (ii) hard positive and pairwise negative mining. On thechallenging Oxford RobotCar night condition, our method outperforms thewell-known triplet loss by 24.4% in localization accuracy within 5m.

Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images