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Appearance learning for 3D pose detection of a satellite at close-range

Oumer, Nassir W. and Kriegel, Simon and Ali, Haider and Reinartz, Peter (2017) Appearance learning for 3D pose detection of a satellite at close-range. ISPRS Journal of Photogrammetry and Remote Sensing, 125, pp. 1-15. Elsevier. DOI: 10.1016/j.isprsjprs.2017.01.002 ISBN 0924-2716 ISSN 0924-2716

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Official URL: http://www.sciencedirect.com/science/article/pii/S0924271617300047


In this paper we present a learning-based 3D detection of a highly challenging specular object exposed to a direct sunlight at very close-range. An object detection is one of the most important areas of image processing, and can also be used for initialization of local visual tracking methods. While the object detection in 3D space is generally a difficult problem, it poses more difficulties when the object is specular and exposed to the direct sunlight as in a space environment. Our solution to a such problem relies on an appearance learning of a real satellite mock-up based on a vector quantization and the vocabulary tree. Our method, implemented on a standard computer (CPU), exploits a full perspective projection model and provides near real-time 3D pose detection of a satellite for close-range approach and manipulation. The time consuming part of the training (feature description, building the vocabulary tree and indexing, depth buffering and back-projection) are performed offline, while a fast image retrieval and 3D-2D registration are performed on-line. In contrast, the state of the art image-based 3D pose detection methods are slower on \{CPU\} or assume a weak perspective camera projection model. In our case the dimension of the satellite is larger than the distance to the camera, hence the assumption of the weak perspective model does not hold. To evaluate the proposed method, the appearance of a full scale mock-up of the rear part of the TerraSAR-X satellite is trained under various illumination and camera views. The training images are captured with a camera mounted on six degrees of freedom robot, which enables to position the camera in a desired view, sampled over a sphere. The views that are not within the workspace of the robot are interpolated using image-based rendering. Moreover, we generate ground truth poses to verify the accuracy of the detection algorithm. The achieved results are robust and accurate even under noise due to specular reflection, and able to initialize a local tracking method.

Item URL in elib:https://elib.dlr.de/113120/
Document Type:Article
Title:Appearance learning for 3D pose detection of a satellite at close-range
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Oumer, Nassir W.nassir.oumer (at) dlr.deUNSPECIFIED
Kriegel, Simonsimon.kriegel (at) dlr.dehttps://orcid.org/0000-0003-4711-8527
Ali, HaiderHaider.Ali (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:March 2017
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.isprsjprs.2017.01.002
Page Range:pp. 1-15
Keywords:Satellite pose detection Pose estimation Pose initialization Appearance learning Feature clustering
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Technik für Raumfahrtsysteme
DLR - Research theme (Project):R - Vorhaben Multisensorielle Weltmodellierung
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Oumer, Nassir
Deposited On:17 Jul 2017 13:17
Last Modified:06 Sep 2019 15:28

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