Assessing Sequential Monoscopic Images for Height Estimation of Fixed-Wing Drones

We design a feature-based model to estimate and predict the free height of a fixed-wing drone flying at altitudes up to 100 meters above terrain using the stereo vision principles and a one-dimensional Kalman filter. We design this using a single RGB camera to assess the viability of sequential images for height estimation, and to assess which issues and pitfalls are likely to affect such a system. This model is tested on both simulation data flying above flat and varying terrain, as well as data from a real test flight. Simulation RMSE ranges from 10.7% to 21.0% of maximum flying height. Real estimates vary significantly more, resulting in an RMSE of 27.55% of median flying height of one test flight. Best MAE was roughly 17%, indicating the error to expect from the system. We conclude that feature-based detection appears to be too heavily influenced by noise introduced by the drone and other uncontrollable parameters to be used in reliable height estimation.