Name
Abstract | ||
|---|---|---|
Finding safe landing zones (SLZ) in urban areas and natural scenes is one of the many challenges that must be overcome in automating Unmanned Aerial Vehicles (UAV) navigation. Using passive vision sensors to achieve this objective is a very promising avenue due to their low cost and the potential they provide for performing simultaneous terrain analysis and 3D reconstruction. In this paper, we propose using a deep learning approach on UAV imagery to assess the SLZ. The model is built on a semantic segmentation architecture whereby thematic classes of the terrain are mapped into safety scores for UAV landing. Contrary to past methods, which use hard classification into safe/unsafe landing zones, our approach provides a continuous safety map that is more practical for an emergency landing. Experiments on public datasets have shown promising results. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CRV55824.2022.00035 | 2022 19th Conference on Robots and Vision (CRV) |
Keywords | DocType | ISBN |
Automatic UAV navigation,safe landing zones (SLZ),semantic segmentation,deep regression | Conference | 978-1-6654-9775-6 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sakineh Abdollahzadeh | 1 | 0 | 0.34 |
| Pier-Luc Proulx | 2 | 0 | 0.34 |
| Mohand Said Allili | 3 | 0 | 0.34 |
| Jean-François Lapointe | 4 | 0 | 0.34 |