ATLoc: Aerial Thermal Images Localization via View Synthesis

Yuxiang Liu, Rouwan Wu, Shen Yan, Xiaoya Cheng, Juelin Zhu, Yu Liu, Maojun Zhang

Abstract: While visual localization has made significant advances in recent years, it still lacks robustness in low-light situations. Thermal camera images, which capture temperature data, provide a potential solution for these environments. However, the scarcity of well-annotated, publicly available datasets for thermal localization, particularly those focused on absolute pose estimation, impedes further advancement in this field. In this research, we introduce a novel dataset that includes six-degree-of-freedom (6-DoF) absolute poses of query images for large-scale, realistic aerial localization of thermal images. Besides, we introduce a render-to-localization pipeline tailored for thermal image localization. This pipeline predicts the 6-DoF pose of a query using a synthetic technique based on geometric refined thermal model. Experimental results demonstrate the effectiveness of our method on this newly proposed dataset. Notably, our method achieves a median position error of less than 1.5 m and a median angle error of less than 1.5° under diverse test conditions. A comprehensive analysis of factors influencing localization accuracy is also provided. Our code and dataset will be available at https://github.com/RingoWRW/ATLoc .