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UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation

Mueller, Markus S.; Jutzi, Boris ORCID iD icon


The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000087401
Veröffentlicht am 13.11.2018
DOI: 10.3390/drones2010007
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2504-446X
KITopen-ID: 1000087401
Erschienen in Drones
Verlag MDPI
Band 2
Heft 1
Seiten 7
Vorab online veröffentlicht am 13.02.2018
Schlagwörter convolutional neural networks; data augmentation; image-based navigation; pose estimation
Nachgewiesen in Dimensions
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