Geo-localisation of aerial images captured with Drones
The Global Position System (GPS) has become an essential sensor for public applications, maritime systems, robotics and aerial vehicles. Traditionally, autonomous flight in outdoor areas is possible thanks to GPS devices that enable Unmanned Aerial Vehicles (UAV) to obtain their position in latitude...
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| Format: | master thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2021 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | English |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/2136 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/2136 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Inspec/Geo-localisation info:eu-repo/classification/Inspec/Aerial Images info:eu-repo/classification/Inspec/UAV Navigation info:eu-repo/classification/Inspec/GPS info:eu-repo/classification/Inspec/Deep Learning info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120323 |
| Summary: | The Global Position System (GPS) has become an essential sensor for public applications, maritime systems, robotics and aerial vehicles. Traditionally, autonomous flight in outdoor areas is possible thanks to GPS devices that enable Unmanned Aerial Vehicles (UAV) to obtain their position in latitude and longitude coordinates. However, GPS may become unreliable when the drone flies in environments where the signal may get occluded. Malicious attacks may also compromise the GPS signal, aiming to block the signal or replace it with spurious data. Motivated by these scenarios, the proposed approach relies on a methodology to estimate the GPS position of a UAV using Convolutional Neural Networks (CNN) and a learningbased strategy. For the latter, we adopted two learning scenarios: 1) offline learning; 2) online learning, where we tackled the re-localisation and geo-localisation problem in scenarios where GPS devices fail. We argue that our approach could be used as a backup system to return the UAV home. Therefore, we performed tests with aerial images and videos captured with the Matrice 100 and Bebop 2 drones in two scenarios with different trajectories to demonstrate our approach using a compact CNN and online learning implementation. The presented experiments with CompactPN got an average error of 2.60 to 6.16 metres and a speed of 107.88 fps. Likewise, online learning implementation based on the AR1* method obtained an accuracy of 0.79 to 0.92 and a speed of 127.33 fps. |
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