Recalling the most relevant visual memories for localisation or understanding a priori the likely outcome of localisation effort against a particular visual memory is useful for efficient and robust visual navigation. Solutions to this problem should be divorced from performance appraisal against ground truth - as this is not available at run-time - and should ideally be based on generalisable environmental observations. For this, we propose applying the recently developed Visual DNA as a highly scalable tool for comparing datasets of images - in this work, sequences of map and live experiences. In the case of localisation, important dataset differences impacting performance are modes of appearance change, including weather, lighting, and season. Specifically, for any deep architecture which is used for place recognition by matching feature volumes at a particular layer, we use distribution measures to compare neuron-wise activation statistics between live images and multiple previously recorded past experiences, with a potentially large seasonal (winter/summer) or time of day (day/night) shift. We find that differences in these statistics correlate to performance when localising using a past experience with the same appearance gap. We validate our approach over the Nordland cross-season dataset as well as data from Oxford's University Parks with lighting and mild seasonal change, showing excellent ability of our system to rank actual localisation performance across candidate experiences.

翻译：为高效鲁棒的视觉导航，在定位时回忆最相关的视觉记忆，或先验理解针对特定视觉记忆进行定位的可能结果，具有重要实用价值。该问题的解决方案应脱离基于真值的性能评估（因为在运行阶段无法获取真值），理想情况下应基于可泛化的环境观测。为此，我们提出将最新开发的"视觉DNA"作为高度可扩展工具，用于比较图像数据集——本工作中特指地图序列与实时体验序列。在定位场景下，影响性能的关键数据集差异表现为外观变化模式，包括天气、光照和季节等。具体而言，对于任何通过匹配特定层特征体积进行地点识别的深度架构，我们采用分布度量方法，比较实时图像与多个预存历史经验之间的神经元激活统计量，这些经验可能包含显著的季节性（冬季/夏季）或昼夜（日间/夜间）偏移。研究发现，当使用具有相同外观差异的历史经验进行定位时，这些统计量的差异与定位性能存在相关性。我们在Nordland跨季节数据集以及包含光照与温和季节变化的牛津大学公园数据集上验证了该方法，结果表明我们的系统能够出色地实现对候选经验的实际定位性能进行排序。