AI 中文总结
该研究针对无人机视觉里程计任务，在合成数据集上评估多种图像匹配方法，发现RoMa匹配器性能最优，SIFT特征表现优于部分最新最先进方法，为无人机视觉导航提供参考。
AI 中文摘要
无人机（UAV）正成为环境监测与运输等众多应用的强大工具，但它们依赖全球导航卫星系统（GNSS）进行导航，在定位信号不可用或中断的场景中易发生灾难性故障。本研究探索视觉里程计（VO）作为关键导航组件，近年来提出了多种基于深度学习的图像匹配方法，但尚未在成熟的VO系统中实现。本文在合成数据集上，针对无人机位置跟踪任务，评估了最新的图像匹配方法，这些方法配备向下朝向的相机，结果发现最新的RoMa匹配器产生最佳结果，而SIFT特征的性能优于部分最新的最先进方法。
英文摘要
Unmanned aerial vehicles (UAVs) are becoming a powerful tool for many environmental monitoring and transport applications. Yet, their reliance on Global Navigation Satellite System (GNSS) technology for navigation makes them susceptible to catastrophic failures in scenarios where the positioning signal is unavailable or disrupted. This work explores Visual Odometry (VO) as a crucial navigation component. Recently, numerous deep-learning-based methods for image matching have been proposed that are yet to be implemented in a fully-fledged VO system. In this paper, we evaluate recent state-of-the-art image matching methods for the task of VO for UAV position tracking, with a downwards-facing camera, on our synthetic dataset, and find that while the best results are generated by the recent RoMa matcher, SIFT features can outperform some recent state-of-the-art.
Comments5 pages, published in the Proceedings of the 33rd International Electrotechnical and Computer Science Conference ERK 2024
Journal refProceedings of the 33rd International Electrotechnical and Computer Science Conference ERK 2024, pp. 479-483, 2024
