Integration of Visual Inertial Odometry (VIO) methods into a modular control system designed for deployment of Unmanned Aerial Vehicles (UAVs) and teams of cooperating UAVs in real-world conditions are presented in this paper. Reliability analysis and fair performance comparison of several methods integrated into a control pipeline for achieving full autonomy in real conditions is provided. Although most VIO algorithms achieve excellent localization precision and negligible drift on artificially created datasets, the aspects of reliability in non-ideal situations, robustness to degraded sensor data, and the effects of external disturbances and feedback control coupling are not well studied. These imperfections, which are inherently present in cases of real-world deployment of UAVs, negatively affect the ability of the most used VIO approaches to output a sensible pose estimation. We identify the conditions that are critical for a reliable flight under VIO localization and propose workarounds and compensations for situations in which such conditions cannot be achieved. The performance of the UAV system with integrated VIO methods is quantitatively analyzed w.r.t. RTK ground truth and the ability to provide reliable pose estimation for the feedback control is demonstrated onboard a UAV that is tracking dynamic trajectories under challenging illumination.

翻译：本文提出将视觉惯性里程计（VIO）方法集成到模块化控制系统中，该系统专为在真实条件下部署无人机及协同无人机编队而设计。文中对多种集成至控制管道的方法进行了可靠性分析及公平性能比较，旨在实现真实环境中的完全自主性。尽管大多数VIO算法在人工生成数据集上实现了卓越的定位精度与可忽略的漂移，但在非理想条件下的可靠性、对退化传感器数据的鲁棒性、外部扰动及反馈控制耦合效应等方面尚未得到充分研究。这些在无人机实际部署中固有存在的缺陷，显著降低了多数主流VIO方法输出合理位姿估计的能力。我们识别了依赖VIO定位实现可靠飞行的关键条件，并针对无法满足这些条件的情况提出了替代方案与补偿措施。通过RTK地面真值对集成VIO方法的无人机系统性能进行了量化分析，并在跟踪动态轨迹的无人机上（于挑战性光照条件下）验证了其为反馈控制提供可靠位姿估计的能力。