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Segmentation-Driven Spacecraft Pose Estimation for Vision-based Relative Navigation in Space

Kajak, Karl Martin and Maddock, Christie and Frei, Heike and Schwenk, Kurt (2021) Segmentation-Driven Spacecraft Pose Estimation for Vision-based Relative Navigation in Space. In: Proceedings of the International Astronautical Congress, IAC. 72nd International Astronautical Congress (IAC 2021), 25.-29. Okt. 2021, Dubai, Vereinigte Arabische Emirate. ISSN 0074-1795.

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Abstract

Vision-based relative navigation technology is a key enabler of several areas of the space industry such as on-orbit servicing, space debris removal, and formation flying. A particularly demanding scenario is navigating relative to a non-cooperative target that does not offer any navigational aid and is unable to stabilize its attitude. Previously, the state-of-the-art in vision-based relative navigation has relied on image processing and template matching techniques. However, outside of the space industry, state-of-the-art object pose estimation techniques are dominated by convolutional neural networks (CNNs). This is due to CNNs flexibility towards arbitrary pose estimation targets, their ability to use whatever available target features, and robustness towards varied lighting conditions, damage to targets, occlusions, and other effects that might interfere with the image. The use of CNNs for visual relative navigation is still relatively unexplored in terms of how their unique advantages can best be exploited. This research aims to integrate a state-of-the-art CNN-based pose estimation architecture in a relative navigation system. The system's navigation performance is benchmarked on realistic images gathered from the European Proximity Operations Simulator 2.0 (EPOS 2.0) robotic hardware-in-the-loop laboratory. A synthetic dataset is generated using Blender as a rendering engine. A segmentation-based 6D pose estimation CNN is trained using the synthetic dataset and the resulting pose estimation performance is evaluated on a set of real images gathered from the cameras of the EPOS 2.0 robotic close-range relative navigation laboratory. It is demonstrated that a synthetic-image-trained CNN-based pose estimation pipeline is able to successfully perform in a close-range visual navigation setting on real camera images of spacecraft that exhibits, though with some limitations that still have to be surpassed for the system to be ready for operation. Furthermore, it is able to do so with a symmetric target, a common difficulty with neural networks in a pose estimation setting.

Item URL in elib:https://elib.dlr.de/185541/
Document Type:Conference or Workshop Item (Lecture)
Title:Segmentation-Driven Spacecraft Pose Estimation for Vision-based Relative Navigation in Space
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kajak, Karl MartinUNSPECIFIEDhttps://orcid.org/0000-0003-3029-0400UNSPECIFIED
Maddock, ChristieUNSPECIFIEDhttps://orcid.org/0000-0003-1079-4863UNSPECIFIED
Frei, HeikeUNSPECIFIEDhttps://orcid.org/0000-0003-0836-9171UNSPECIFIED
Schwenk, KurtUNSPECIFIEDhttps://orcid.org/0000-0002-4305-9702UNSPECIFIED
Date:October 2021
Journal or Publication Title:Proceedings of the International Astronautical Congress, IAC
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
ISSN:0074-1795
Status:Published
Keywords:close-range relative navigation, pose estimation, symmetric uncooperative target, monocular camera, convolutional neural network, domain randomization
Event Title:72nd International Astronautical Congress (IAC 2021)
Event Location:Dubai, Vereinigte Arabische Emirate
Event Type:international Conference
Event Dates:25.-29. Okt. 2021
Organizer:International Astronautical Federation (IAF)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - On-Orbit Servicing [SY]
Location: Oberpfaffenhofen
Institutes and Institutions:Space Operations and Astronaut Training > Space Flight Technology
Deposited By: Kajak, Karl Martin
Deposited On:11 Mar 2022 10:25
Last Modified:11 Mar 2022 10:25

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