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Absolute Anchor-Negative Distance Based Metric Learning for Day-Night Feature Matching

Accurate and robust day-night feature matching is a fundamental challenge for visual localization of autonomous driving cars. Under adverse illumination changing situations, the performance of current handcrafted and CNN-based local features will degrade severely. This problem is caused by the phenomenon of domain gap, which can be alleviated by applying domain transformation to increase the discriminativeness of feature descriptors. However, the standard optimization function of descriptors with hard negative triplets will lead to local minima, which will significantly decrease the robustness of domain transformation on feature matching. To address above challenges, the absolute anchor-negative distance based loss function is proposed, which is named AAN loss. The proposed AAN loss integrates the absolute distance of anchor-negative and anchor-positive samples, to effectively strengthen the discriminativeness of descriptors and prevent converging to local minima. Our proposed method can improve the convergence of the domain transformation and effectively improve the performance of feature matching under adverse illumination conditions. Extensive experiments and evaluations show the improved robustness and efficiency of the proposed method.