DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based mapping

Abstract

Automated Aerial Triangulation (AAT), aiming to restore image poses and reconstruct sparse points simultaneously, plays a pivotal role in earth observation. AAT has evolved into a fundamental process widely applied in large-scale Unmanned Aerial Vehicle (UAV) based mapping. However classic AAT methods still face challenges like low efficiency and limited robustness. This paper introduces DeepAAT, a deep learning network designed specifically for AAT of UAV imagery. DeepAAT considers both spatial and spectral characteristics of imagery, enhancing its capability to resolve erroneous matching pairs and accurately predict image poses. DeepAAT marks a significant leap in AAT’s efficiency, ensuring thorough scene coverage and precision. Its processing speed outpaces incremental AAT methods by hundreds of times and global AAT methods by tens of times while maintaining a comparable level of reconstruction accuracy. Additionally, DeepAAT’s scene clustering and merging strategy facilitate rapid localization and pose determination for large-scale UAV images, even under constrained computing resources. The experimental results demonstrate that DeepAAT substantially improves over conventional AAT methods, highlighting its potential for increased efficiency and accuracy in UAV-based 3D reconstruction tasks. To benefit the photogrammetry society, the code of DeepAAT will be released at: https://github.com/WHU-USI3DV/DeepAAT.

Keywords

• Automated Aerial Triangulation (AAT)
• Unmanned Aerial Vehicle (UAV)
• Structure from Motion (sfM)
• Orientation