Kalman Filter-Based Drift Detection and Mitigation of Visual-Inertial Odometry in UAVs

We present a conceptual framework for an autonomous safety mechanism designed to enhance the reliability of Unmanned Aerial Vehicles (UAVs) that use Visual-Inertial Odometry (VIO) for state estimation. As UAVs increasingly interact with the public, such safety mechanisms are crucial to reducing the likelihood and severity of accidents. VIO drift, which occurs when accumulated estimation errors cause discrepancies between the UAV’s perceived and actual position, poses a significant risk to safe operation. To address this challenge, we propose a Kalman filter-based approach for detecting VIO drift events. Upon detection, the envisioned safety mechanism is designed to adjust state estimation by integrating onboard gyroscope measurements and thrust commands for short durations, aiming to enhance stability and prevent potential crashes before initiating a controlled landing. While this framework provides the foundation for a real-time safety mechanism, the implementation and experiment focus on validating the drift detection component in an offline setting using real UAV flight data. The results demonstrate the effectiveness of the detection method in identifying VIO drift scenarios, highlighting its potential for future real-time applications.