Monocular Depth Estimation for Autonomous UAV Navigation Based on Deep Learning
- This study focuses on the autonomous navigation and mapping of indoor environments using a drone equipped only with a monocular camera and height measurement sensors. A visual SLAM algorithm was employed to generate a preliminary map of the environment and to determine the drone's position within the map. A deep neural network was utilized to generate a depth image from the monocular camera'sThis study focuses on the autonomous navigation and mapping of indoor environments using a drone equipped only with a monocular camera and height measurement sensors. A visual SLAM algorithm was employed to generate a preliminary map of the environment and to determine the drone's position within the map. A deep neural network was utilized to generate a depth image from the monocular camera's input, which was subsequently transformed into a point cloud to be projected into the map. By aligning the depth point cloud with the map, 3D occupancy grid maps were constructed by using ray tracing techniques to get a precise depiction of obstacles and the surroundings. Due to the absence of IMU data from the low-cost drone for the SLAM algorithm, the created maps are inherently unscaled. However, preliminary tests with relative navigation in unscaled maps have revealed potential accuracy issues, which can only be overcome by incorporating additional information from the given sensors for scale estimation.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8284 | Bibliografische Angaben |
Title (English): | Monocular Depth Estimation for Autonomous UAV Navigation Based on Deep Learning |
Conference: | International Scientific Conference Electronics (32. : 13-15 September 2023 : Sozopol, Bulgaria) |
Author: | Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Anton Dreher, Dimitre Trendafilov |
Year of Publication: | 2023 |
Publisher: | IEEE |
First Page: | 1 |
Last Page: | 6 |
Parent Title (English): | 2023 XXXII International Scientific Conference Electronics (ET) |
ISBN: | 979-8-3503-0200-4 (Elektronisch) |
ISBN: | 979-8-3503-0201-1 (Print on Demand) |
DOI: | https://doi.org/10.1109/ET59121.2023.10279533 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Forschung / IMLA - Institute for Machine Learning and Analytics | |
Institutes: | Bibliografie |
Tag: | Monocular Depth Estimation | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index < 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |