Accurate pose estimation is fundamental for unmanned aerial vehicle (UAV) applications, where Visual-Inertial SLAM (VI-SLAM) provides a cost-effective solution for localization and mapping. However, existing VI-SLAM methods mainly rely on sensors with limited fields of view (FoV), which can lead to drift and even failure in complex UAV scenarios. Although panoramic cameras provide omnidirectional perception to improve robustness, panoramic VI-SLAM and corresponding real-world datasets for UAVs remain underexplored. To address this limitation, we first construct a real-world panoramic visual-inertial dataset covering diverse flight conditions, including varying illumination, altitudes, trajectory lengths, and motion dynamics. To achieve accurate and robust pose estimation under such challenging UAV scenarios, we propose a panoramic VI-SLAM framework that exploits the omnidirectional FoV via the proposed panoramic feature extraction and panoramic loop closure, enhancing feature constraints and ensuring global consistency. Extensive experiments on both the proposed dataset and public benchmarks demonstrate that our method achieves superior accuracy, robustness, and consistency compared to existing approaches. Moreover, deployment on embedded platform validates its practical applicability, achieving comparable computational efficiency to PC implementations. The source code and dataset are publicly available at https://drive.google.com/file/d/1lG1Upn6yi-N6tYpEHAt6dfR1uhzNtWbT/view

翻译：[译] 精确的位姿估计是无人机应用的基础，其中视觉-惯性SLAM系统为定位与建图提供了经济高效的解决方案。然而现有VI-SLAM方法主要依赖有限视场角的传感器，在复杂无人机场景中易产生漂移甚至失效。尽管全景相机通过全向感知增强鲁棒性，但面向无人机的全景VI-SLAM及其对应实景数据集仍鲜有探索。针对这一局限，我们首先构建了覆盖不同飞行条件（包括光照变化、飞行高度、轨迹长度及运动动态）的实景全景视觉-惯性数据集。为在严苛无人机场景下实现精确鲁棒的位姿估计，我们提出全景VI-SLAM框架，通过所设计的全景特征提取与全景闭环检测方法，利用全向视场角增强特征约束并保证全局一致性。在自主构建数据集与公开基准上的大量实验表明，相较现有方法，我们的方法在精度、鲁棒性与一致性方面均表现更优。此外，嵌入式平台部署验证了其实际可用性，实现了与PC实现相当的计算效率。源代码与数据集公开于 https://drive.google.com/file/d/1lG1Upn6yi-N6tYpEHAt6dfR1uhzNtWbT/view