What you see is what you get: Experience ranking with deep neural
  dataset-to-dataset similarity for topological localisation
Matthew Gadd Benjamin Ramtoula Daniele De Martini Paul Newman
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.