Knowledge Commons of Institute of Automation，CAS
| Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization | |
| Hanjiang Hu; Hesheng Wang; Zhe Liu; Weidong Chen | |
| Source Publication | IEEE/CAA Journal of Automatica Sinica
 |
| ISSN | 2329-9266 |
| 2022 | |
| Volume | 9Issue:2Pages:313-328 |
| Abstract | 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. |
| Keyword | Deep representation learning place recognition visual localization |
| DOI | 10.1109/JAS.2021.1003907 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/45992 |
| Collection | 学术期刊_IEEE/CAA Journal of Automatica Sinica |
| Recommended Citation GB/T 7714 | Hanjiang Hu,Hesheng Wang,Zhe Liu,et al. Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(2):313-328. |
| APA | Hanjiang Hu,Hesheng Wang,Zhe Liu,&Weidong Chen.(2022).Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization.IEEE/CAA Journal of Automatica Sinica,9(2),313-328. |
| MLA | Hanjiang Hu,et al."Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization".IEEE/CAA Journal of Automatica Sinica 9.2(2022):313-328. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| JAS-2020-1028.pdf（20920KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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