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Efficient and robust 3D object reconstruction based on monocular SLAM and CNN semantic segmentation

  • Various applications implement slam technology, especially in the field of robot navigation. We show the advantage of slam technology for independent 3d object reconstruction. To receive a point cloud of every object of interest void of its environment, we leverage deep learning. We utilize recent cnn deep learning research for accurate semantic segmentation of objects. In this work, we propose two fusion methods for cnn-based semantic segmentation and slam for the 3d reconstruction of objects of interest in order to obtain a more robustness and efficiency. As a major novelty, we introduce a cnn-based masking to focus slam only on feature points belonging to every single object. Noisy, complex or even non-rigid features in the background are filtered out, improving the estimation of the camera pose and the 3d point cloud of each object. Our experiments are constrained to the reconstruction of industrial objects. We present an analysis of the accuracy and performance of each method and compare the two methods describing their pros and cons.

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Metadaten
Name:Weber, Thomas; Triputen, Sergey; Gopal, Atmaraaj; Eißler, Steffen; Höfert, Christian; Rätsch, Matthias
DOI:https://doi.org/10.1007/978-3-030-35699-6_27
Erschienen in:RoboCup 2019: Robot World Cup XXIII. - (Lecture notes in artificial intelligence ; 11531)
Publisher:Springer
Place of publication:Cham
Editor:Stephan Chalup
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Tag:3d reconstruction; bin-picking; cnn; collaborative robot; depth estimation; lsd-slam; monocular camera; semantic segmentation; slam
Pagenumber:13
First Page:351
Last Page:363
City of event:Sydney, Australien
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:006 Spezielle Computerverfahren
Open Access:Nein
Licence (German):License Logo  Lizenzbedingungen Springer