見たままが得られるもの：深層ニューラルデータセット間類似度による経験ランキングとトポロジカルローカリゼーション
What you see is what you get: Experience ranking with deep neural dataset-to-dataset similarity for topological localisation
画像データセット間の類似度を測るVisual DNAを用いて、異なる天候・照明・季節の下でのローカリゼーション性能を事前にランク付けする手法を提案し、その有効性を検証した。
著者: Matthew Gadd, Benjamin Ramtoula, Daniele De Martini, Paul Newman
分類: cs.CV, cs.RO
原文アブストラクト
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.
