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Verlagsausgabe
DOI: 10.5445/IR/1000087399
Veröffentlicht am 13.11.2018
Originalveröffentlichung
DOI: 10.5194/isprs-annals-IV-1-117-2018

CNN-Based Initial Localization Improved by Data Augmentation

Mueller, Markus; Metzger, Alexander; Jutzi, Boris

Abstract:
Image-based localization or camera re-localization is a fundamental task in computer vision and mandatory in the fields of navigation for robotics and autonomous driving or for virtual and augmented reality. Such image pose regression in 6 Degrees of Freedom (DoF) is recently solved by Convolutional Neural Networks (CNNs). However, already well-established methods based on feature matching still score higher accuracies so far. Therefore, we want to investigate how data augmentation could further improve CNN-based pose regression. Data augmentation is a valuable technique to boost performance on training based methods and wide spread in the computer vision community. Our aim in this paper is to show the benefit of data augmentation for pose regression by CNNs. For this purpose images are rendered from a 3D model of the actual test environment. This model again is generated by the original training data set, whereas no additional information nor data is required. Furthermore we introduce different training sets composed of rendered and real images. It is shown that the enhanced training of CNNs by utilizing 3D models of the environmen ... mehr


Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-873991
KITopen-ID: 1000087399
Erschienen in ISPRS TC I Mid-term Symposium “Innovative Sensing – From Sensors to Methods and Applications”, 10–12 October 2018, Karlsruhe, Germany
Verlag ISPRS
Seiten 117-124
Serie ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; IV-1
Schlagworte Convolutional Neural Networks, Data Augmentation, Localization, Navigation, Pose Regression
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