乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T05:19:25Z","timestamp":1737263965020,"version":"3.33.0"},"reference-count":103,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"national funds through Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia (FCT)","award":["LA\/P\/0083\/2020","UIDP\/50009\/2020"]},{"name":"Laboratory of Robotics and Engineering Systems (LARSyS)","award":["UIDB\/50009\/2020"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV\u2019s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV\u2019s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.<\/jats:p>","DOI":"10.3390\/robotics13080114","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T11:54:03Z","timestamp":1722254043000},"page":"114","source":"Crossref","is-referenced-by-count":0,"title":["Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8079-9451","authenticated-orcid":false,"given":"Nuno","family":"Pessanha Santos","sequence":"first","affiliation":[{"name":"Portuguese Military Research Center (CINAMIL), Portuguese Military Academy (Academia Militar), R. Gomes Freire 203, 1169-203 Lisbon, Portugal"},{"name":"Institute for Systems and Robotics (ISR), Instituto Superior T\u00e9cnico (IST), Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal"},{"name":"Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Base Naval de Lisboa, Alfeite, 2800-001 Almada, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chaurasia, R., and Mohindru, V. (2021). Unmanned aerial vehicle (UAV): A comprehensive survey. Unmanned Aerial Vehicles for Internet of Things (IoT) Concepts, Techniques, and Applications, Wiley.","DOI":"10.1002\/9781119769170.ch1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8615367","DOI":"10.1155\/2021\/8615367","article-title":"Energy-efficient unmanned aerial vehicle (UAV) surveillance utilizing artificial intelligence (AI)","volume":"2021","author":"Do","year":"2021","journal-title":"Wirel. Commun. 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