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Domain adaptation and generative modelling have collectively mitigated theexpensive nature of data collection and labelling by leveraging the richabundance of accurate, labelled data in simulation environments. In this work,we study the performance gap that exists between representations optimised forlocalisation on simulation environments and the application of suchrepresentations in a real-world setting. Our method exploits the sharedgeometric similarities between simulation and real-world environments whilstmaintaining invariance towards visual discrepancies. This is achieved byoptimising a representation extractor to project both simulated and realrepresentations into a shared representation space. Our method uses asymmetrical adversarial approach which encourages the representation extractorto conceal the domain that features are extracted from and simultaneouslypreserves robust attributes between source and target domains that arebeneficial for localisation. We evaluate our method by adapting representationsoptimised for indoor Habitat simulated environments (Matterport3D and Replica)to a real-world indoor environment (Active Vision Dataset), showing that itcompares favourably against fully-supervised approaches.

Localising In Complex Scenes Using Balanced Adversarial Adaptation