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Volume 9 Issue 2
Feb.  2022

IEEE/CAA Journal of Automatica Sinica

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Hanjiang Hu, Hesheng Wang, Zhe Liu and Weidong Chen, "Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization," IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 313-328, Feb. 2022. doi: 10.1109/JAS.2021.1003907
Citation: Hanjiang Hu, Hesheng Wang, Zhe Liu and Weidong Chen, "Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization," IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 313-328, Feb. 2022. doi: 10.1109/JAS.2021.1003907

Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

doi: 10.1109/JAS.2021.1003907
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  • Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localization methods. Due to the drastic variability of environmental conditions, e.g., illumination changes, retrieval-based visual localization is severely affected and becomes a challenging problem. In this work, a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image translation. Then, a novel gradient-weighted similarity activation mapping loss (Grad-SAM) is incorporated for finer localization with high accuracy. We also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised manner. The final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM loss. Extensive experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons dataset. The strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons dataset. Our performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision, especially under challenging environments with illumination variance, vegetation, and night-time images. Moreover, real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

     

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    Highlights

    • A domain-invariant feature learning framework is proposed based with feature consistency loss
    • A new gradient-weighted similarity activation map is proposed for high-accuracy retrieval
    • A novel self--supervised contrastive learning is proposed with adaptive triplet
    • Our results keep on par with state-of-the-art baselines with efficient two-stage pipeline

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