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.
@article{arxiv.2108.08516,
title = {Retrieval and Localization with Observation Constraints},
author = {Yuhao Zhou and Huanhuan Fan and Shuang Gao and Yuchen Yang and Xudong Zhang and Jijunnan Li and Yandong Guo},
journal= {arXiv preprint arXiv:2108.08516},
year = {2021}
}
Comments
Accepted by the 2021 International Conference on Robotics and Automation (ICRA2021)