对比多种图像匹配方法在无人机视觉里程计中的表现。
Evaluation of Image Matching Methods for Visual Odometry on UAVs
- 在合成数据集上测试最新图像匹配算法的视觉里程计性能。
- RoMa匹配器表现最佳，但传统SIFT在部分场景更优。
- 为无人机导航系统选型提供实证参考，适合视觉定位研究者。
无人飞行器（UAV）在环境监测和运输等应用中日益重要，但其依赖全球导航卫星系统（GNSS）进行导航，在信号缺失或受干扰时易发生严重故障。本文探索视觉里程计（VO）作为关键导航组件的可行性。近年来，众多基于深度学习的图像匹配方法被提出，但尚未在完整的VO系统中实现。本文在自建合成数据集上，评估了最新最先进的图像匹配方法在下视摄像头的无人机位置追踪任务中的表现。结果表明，尽管最新的RoMa匹配器表现最优，但传统SIFT特征在某些情况下仍能超越部分先进方法。
原文摘要 · Abstract (English)
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.
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