Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Editors | Andreas Kerren, Christophe Hurter, Jose Braz |
Number of pages | 9 |
Volume | 5 |
Publisher | SCITEPRESS Digital Library |
Publication date | Feb 2019 |
Pages | 751-759 |
ISBN (Electronic) | 978-989-758-354-4 |
DOIs | |
Publication status | Published - Feb 2019 |
Event | 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Visigrapp 2019) - Prague, Czech Republic Duration: 25 Feb 2019 → 27 Feb 2019 |
Conference
Conference | 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Visigrapp 2019) |
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Country/Territory | Czech Republic |
City | Prague |
Period | 25/02/2019 → 27/02/2019 |
Keywords
- Computer Vision
- Drone
- Feature Detection
- Free Height Estimation
- Stereo Equation
- UAV