Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

TL;DR

This paper proposes a novel approach using coupled GANs with cyclic consistency to improve place recognition across seasons by translating appearance domains without requiring paired images.

Contribution

It introduces a domain translation method with coupled GANs and cyclic consistency for robust place recognition under severe appearance changes.

Findings

Effective cross-season place recognition demonstrated

Learned feature spaces correlate with visual similarity

Method outperforms traditional recognition techniques

Abstract

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.