生成的敵対ネットワークによる困難な場所認識タスクへの取り組み
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
2つのGANを組み合わせ、季節間の画像対応なしに一方の見た目を他方へ変換し、学習した特徴空間で季節をまたぐ場所認識を実現した。
著者: Yasir Latif, Ravi Garg, Michael Milford, Ian Reid
分類: cs.RO
原文アブストラクト
Place recognition is an essential component of Simultaneous Localization And Mapping (SLAM). Under severe appearance change, reliable place recognition is a difficult perception task since the same place is perceptually very different in the morning, at night, or over different seasons. This work addresses place recognition as a domain translation task. Using a pair of coupled Generative Adversarial Networks (GANs), we show that it is possible to generate the appearance of one domain (such as summer) from another (such as winter) without requiring image-to-image correspondences across the domains. Mapping between domains is learned from sets of images in each domain without knowing the instance-to-instance correspondence by enforcing a cyclic consistency constraint. In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition. Experiments show that learned features correspond to visual similarity and can be effectively used for place recognition across seasons.
