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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.

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