UAV-SEAD: State Estimation Anomaly Dataset for UAVs

2026-02-14

AI Summary

This work addresses the critical challenge of anomaly detection in unmanned aerial vehicle (UAV) state estimation, where undetected anomalies can lead to mission deviations and compromise system safety and reliability. To this end, the authors present the first large-scale, real-world anomaly dataset collected from 1,396 flights (over 52 hours) across diverse indoor and outdoor environments and sensor configurations, without synthetic anomaly injection. Built on the PX4 flight control platform, the dataset integrates multimodal sensor data—including IMU, GPS, barometer, magnetometer, distance sensors, visual odometry, and optical flow—and introduces a four-category anomaly taxonomy: mechanical-electrical, external positioning, global positioning, and altitude anomalies. This high-fidelity benchmark enables the development, training, and evaluation of state estimation anomaly detection and isolation algorithms, filling a crucial data gap in UAV reliability research.

Technology Category

• Intelligent Robots: State Estimation
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• UAV
• state estimation
• anomaly detection
• flight safety
• sensor data

Innovation

• UAV state estimation
• anomaly detection
• real-world dataset
• sensor fusion
• PX4