Retrieval and Localization with Observation Constraints

• URL: http://arxiv.org/abs/2108.08516v1
• Date: Thu, 19 Aug 2021 06:14:33 GMT
• Title: Retrieval and Localization with Observation Constraints
• Authors: Yuhao Zhou, Huanhuan Fan, Shuang Gao, Yuchen Yang, Xudong Zhang, Jijunnan Li, Yandong Guo
• Abstract summary: We propose an integrated visual re-localization method called RLOCS. It combines image retrieval, semantic consistency and geometry verification to achieve accurate estimations. Our method achieves many performance improvements on the challenging localization benchmarks.
• Score: 12.010135672015704
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate visual re-localization is very critical to many artificial intelligence applications, such as augmented reality, virtual reality, robotics and autonomous driving. To accomplish this task, we propose an integrated visual re-localization method called RLOCS by combining image retrieval, semantic consistency and geometry verification to achieve accurate estimations. The localization pipeline is designed as a coarse-to-fine paradigm. In the retrieval part, we cascade the architecture of ResNet101-GeM-ArcFace and employ DBSCAN followed by spatial verification to obtain a better initial coarse pose. We design a module called observation constraints, which combines geometry information and semantic consistency for filtering outliers. Comprehensive experiments are conducted on open datasets, including retrieval on R-Oxford5k and R-Paris6k, semantic segmentation on Cityscapes, localization on Aachen Day-Night and InLoc. By creatively modifying separate modules in the total pipeline, our method achieves many performance improvements on the challenging localization benchmarks.

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