Visual place recognition is challenging because there are so many factors that can cause the appearance of a place to change, from day-night cycles to seasonal change to atmospheric conditions. In recent years a large range of approaches have been developed to address this challenge including deep-learnt image descriptors, domain translation, and sequential filtering, all with shortcomings including generality and velocity-sensitivity. In this letter we propose a novel descriptor derived from tracking changes in any learned global descriptor over time, dubbed Delta Descriptors. Delta Descriptors mitigate the offsets induced in the original descriptor matching space in an unsupervised manner by considering temporal differences across places observed along a route. Like all other approaches, Delta Descriptors have a shortcoming - volatility on a frame to frame basis - which can be overcome by combining them with sequential filtering methods. Using two benchmark datasets, we first demonstrate the high performance of Delta Descriptors in isolation, before showing new state-of-the-art performance when combined with sequence-based matching. We also present results demonstrating the approach working with four different underlying descriptor types, and two other beneficial properties of Delta Descriptors in comparison to existing techniques: their increased inherent robustness to variations in camera motion and a reduced rate of performance degradation as dimensional reduction is applied. Source code is made available at https://github.com/oravus/DeltaDescriptors.

中文翻译：

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Delta 描述符：基于变化的位置表示，用于稳健的视觉定位

视觉地点识别具有挑战性，因为有很多因素会导致地点外观发生变化，从昼夜循环到季节变化再到大气条件。近年来，已经开发了大量方法来应对这一挑战，包括深度学习图像描述符、域转换和顺序过滤，但所有方法都存在通用性和速度敏感性等缺点。在这封信中，我们提出了一种新颖的描述符，该描述符源自跟踪任何学习到的全局描述符随时间的变化，称为 Delta 描述符。Delta Descriptors 通过考虑沿路线观察到的地点之间的时间差异，以无监督的方式减轻在原始描述符匹配空间中引起的偏移。像所有其他方法一样，Delta Descriptors 有一个缺点——帧到帧的波动性——可以通过将它们与顺序过滤方法结合来​​克服。使用两个基准数据集，我们首先单独展示了 Delta Descriptor 的高性能，然后在与基于序列的匹配相结合时展示了新的最先进的性能。我们还展示了使用四种不同底层描述符类型的方法的结果，以及与现有技术相比，Delta 描述符的另外两个有益特性：它们对相机运动变化的固有鲁棒性增加，以及应用降维时性能下降的速度降低. 源代码可从 https://github.com/oravus/DeltaDescriptors 获得。