Accurate state estimation in Unmanned Aerial Vehicles (UAVs) is crucial for ensuring reliable and safe operation, as anomalies occurring during mission execution may induce discrepancies between expected and observed system behaviors, thereby compromising mission success or posing potential safety hazards. It is essential to continuously monitor and detect such conditions in order to ensure a timely response and maintain system reliability. In this work, we focus on UAV state estimation anomalies and provide a large-scale real-world UAV dataset to facilitate research aimed at improving the development of anomaly detection. Unlike existing datasets that primarily rely on injected faults into simulated data, this dataset comprises 1396 real flight logs totaling over 52 hours of flight time, collected across diverse indoor and outdoor environments using a collection of PX4-based UAVs equipped with a variety of sensor configurations. The dataset comprises both normal and anomalous flights without synthetic manipulation, making it uniquely suitable for realistic anomaly detection tasks. A structured classification is proposed that categorizes UAV state estimation anomalies into four classes: mechanical and electrical, external position, global position, and altitude anomalies. These classifications reflect collective, contextual, and outlier anomalies observed in multivariate sensor data streams, including IMU, GPS, barometer, magnetometer, distance sensors, visual odometry, and optical flow, that can be found in the PX4 logging mechanism. It is anticipated that this dataset will play a key role in the development, training, and evaluation of anomaly detection and isolation systems to address the critical gap in UAV reliability research.

翻译：无人机（UAV）的精确状态估计对于确保可靠与安全运行至关重要，因为任务执行过程中出现的异常可能导致预期系统行为与观测行为之间出现偏差，从而影响任务成功率或引发潜在安全隐患。为确保及时响应并维持系统可靠性，持续监测与检测此类状态异常至关重要。本研究聚焦于无人机状态估计异常，并提供了一个大规模真实世界无人机数据集，以促进旨在改进异常检测技术发展的研究。与现有主要依赖在仿真数据中注入故障的数据集不同，本数据集包含1396条真实飞行日志，总飞行时长超过52小时，采集自多种室内外环境，使用一系列基于PX4的无人机平台，并配备了多种传感器配置。该数据集包含正常与异常飞行数据，且未经过合成处理，因而特别适用于真实的异常检测任务。我们提出了一种结构化分类方法，将无人机状态估计异常分为四类：机械与电气异常、外部位置异常、全局位置异常以及高度异常。这些分类反映了在多变量传感器数据流（包括IMU、GPS、气压计、磁力计、距离传感器、视觉里程计与光流传感器）中观测到的集体性、上下文相关及离群点异常，这些数据均可通过PX4日志机制获取。预计本数据集将在异常检测与隔离系统的开发、训练与评估中发挥关键作用，以弥补无人机可靠性研究中的重要空白。