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

Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT), D-76131 Karlsruhe, Germany
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 24 January 2018 / Accepted: 5 February 2018 / Published: 13 February 2018
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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. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set. View Full-Text
Keywords: convolutional neural networks; data augmentation; image-based navigation; pose estimation convolutional neural networks; data augmentation; image-based navigation; pose estimation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Mueller, M.S.; Jutzi, B. UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation. Drones 2018, 2, 7.

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