Appearance-Invariant 6-DoF Visual Localization using Generative Adversarial Networks

• URL: http://arxiv.org/abs/2012.13191v1
• Date: Thu, 24 Dec 2020 10:43:43 GMT
• Title: Appearance-Invariant 6-DoF Visual Localization using Generative Adversarial Networks
• Authors: Yimin Lin, Jianfeng Huang, Shiguo Lian
• Abstract summary: We propose a novel visual localization network when outside environment has changed such as different illumination, weather and season. The visual localization network is composed of a feature extraction network and pose regression network. Results show that our method outperforms state-of-the-art methods in the scenarios with various environment changes.
• Score: 7.04719493717788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel visual localization network when outside environment has changed such as different illumination, weather and season. The visual localization network is composed of a feature extraction network and pose regression network. The feature extraction network is made up of an encoder network based on the Generative Adversarial Network CycleGAN, which can capture intrinsic appearance-invariant feature maps from unpaired samples of different weathers and seasons. With such an invariant feature, we use a 6-DoF pose regression network to tackle long-term visual localization in the presence of outdoor illumination, weather and season changes. A variety of challenging datasets for place recognition and localization are used to prove our visual localization network, and the results show that our method outperforms state-of-the-art methods in the scenarios with various environment changes.

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