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 paper 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 a second different underlying descriptor type, 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 will be released upon publication.

本研究提出一种基于监控学习全局描述符跟踪变化的新型描述符-Delta描述符，通过考虑行进路线上观察到的地点之间的时间差异，以无监督的方式缓解原始描述符匹配空间引起的偏移。我们使用两个基准数据集演示了Delta描述符在孤立情况下的高性能，进一步通过融合序列匹配方法实现了最新的性能表现，在相机运动方面具有更强的内在鲁棒性，并降低了维数降低时的性能退化率。

Delta描述符：基于变化的位置表示用于强健的视觉定位