联合优化的无人机全局-局部视觉定位
Jointly Optimized Global-Local Visual Localization of UAVs
AI总结：
针对GNSS失效下无人机定位难题，提出GLVL两阶段网络，结合全局检索与局部匹配，实现实时高精度定位，在稀疏纹理场景中误差仅2.39米。
摘要：
当全球导航卫星系统（GNSS）受到干扰且不可靠时，无人机的导航和定位面临挑战。传统技术，如同时定位与地图构建（SLAM）和视觉里程计（VO），在提供绝对坐标和减轻误差累积方面存在一定局限性。现有的视觉定位方法通过与正射卫星图像匹配来实现无误差累积的自主视觉定位。然而，由于复杂的匹配过程，这样做无法保证实时性能。为了解决这些挑战，我们提出了一种新颖的全局-局部视觉定位（GLVL）网络。我们的GLVL网络是一种两阶段视觉定位方法，结合了一个大规模检索模块，用于查找与无人机飞行场景相似的区域，以及一个细粒度匹配模块，用于定位无人机的精确坐标，从而实现实时且精确的定位。训练过程以端到端的方式联合优化，以进一步增强模型能力。在涵盖纹理丰富和纹理稀疏区域的六个无人机飞行场景上的实验表明，我们的模型能够满足无人机实时精确定位的需求。特别是，在纹理特征稀疏的村庄场景中，我们的方法在0.48秒内实现了仅2.39米的定位误差。
英文摘要：
Navigation and localization of UAVs present a challenge when global navigation satellite systems (GNSS) are disrupted and unreliable. Traditional techniques, such as simultaneous localization and mapping (SLAM) and visual odometry (VO), exhibit certain limitations in furnishing absolute coordinates and mitigating error accumulation. Existing visual localization methods achieve autonomous visual localization without error accumulation by matching with ortho satellite images. However, doing so cannot guarantee real-time performance due to the complex matching process. To address these challenges, we propose a novel Global-Local Visual Localization (GLVL) network. Our GLVL network is a two-stage visual localization approach, combining a large-scale retrieval module that finds similar regions with the UAV flight scene, and a fine-grained matching module that localizes the precise UAV coordinate, enabling real-time and precise localization. The training process is jointly optimized in an end-to-end manner to further enhance the model capability. Experiments on six UAV flight scenes encompassing both texture-rich and texture-sparse regions demonstrate the ability of our model to achieve the real-time precise localization requirements of UAVs. Particularly, our method achieves a localization error of only 2.39 meters in 0.48 seconds in a village scene with sparse texture features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
