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[bibtex]@InProceedings{Holzemann_2025_WACV, author = {H\"olzemann, Henry and Fiolka, Torsten}, title = {Semantic Clustering of Image Retrieval Databases used for Visual Localization}, booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)}, month = {February}, year = {2025}, pages = {6998-7007} }
Semantic Clustering of Image Retrieval Databases used for Visual Localization
Abstract
Accurate self-localization of unmanned aerial systems (UAS) is needed to reduce their dependency on global navigation satellite systems (GNSS). Image retrieval techniques comparing aerial images with a reference database can be used for visual localization (VL). But the search space may be vast and a full search not feasible on a small UAS. In this work we propose a novel solution that divides the reference database into smaller clusters based on the semantic content of images. To this end we generate and make use of a dataset for semantic segmentation of aerial image captures. By characterizing scenes and objects in images semantically retrieval-based systems are able to differentiate images and scenes efficiently. Using a divide-and-conquer approach images with similar semantics are matched within smaller partial databases. This technique leads to reduced search times and approaches VL as a feasible solution for UAS localization in large-scale outdoor environments.
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