The proposed prediction-based optical flow algorithm, while innovative, may encounter several limitations and failure modes that could affect its performance in deep hazardous tunnels. One significant limitation is the reliance on historical data for predicting missing flow values. If the previous measurements are inaccurate or if there is a sudden change in the UAV's motion (e.g., rapid acceleration or deceleration), the predictions may lead to erroneous distance estimations.
Another potential failure mode arises from the algorithm's sensitivity to environmental conditions. In tunnels with highly reflective surfaces or varying textures, the optical flow sensor may struggle to capture reliable pixel flow data, resulting in frequent instances of zero flow values. This can lead to a cascade of inaccuracies in the predicted velocities and, consequently, the distance measurements.
To address these limitations, several strategies can be implemented. First, incorporating a more sophisticated filtering technique, such as a Kalman filter, could help smooth out the noise in the optical flow data and improve the accuracy of the predictions. This would allow the algorithm to weigh the reliability of past measurements more effectively, reducing the impact of outliers.
Additionally, implementing a fallback mechanism that utilizes IMU data when optical flow quality is poor could enhance the robustness of the system. By continuously monitoring the quality of the optical flow measurements, the algorithm could switch to using IMU-derived estimates when the flow quality falls below a certain threshold. This hybrid approach would ensure that the UAV maintains accurate distance measurements even in challenging conditions.