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Robust 3D Object Detection

Sharif, Helia and Pfaab, Christian and Hölzel, Matthew (2017) Robust 3D Object Detection. The European Space Agency (ESA)'s Automation and Robotics Department. Advanced Space Technologies for Robotics and Automation (ASTRA), 20-22 June 2017, Leiden, The Netherlands.

Full text not available from this repository.

Official URL: https://robotics.estec.esa.int/ASTRA/Astra2017/0.%20Tuesday%2020%20June/1B%20Sensors%20and%20Perception/S.1B_14.00_Sharif.pdf


One of the major challenges for unmanned space exploration is the latency caused by communication delays, making tasks such as docking difficult due to limited possibility for human intervention. In this paper, we address this issue by proposing an image processing technique capable of real-time, lowpower, robust, full 3D object and orientation detection. Oriented Fast and Rotated Brief (ORB) feature detector was selected as the ideal object detection technique for this study. ORB requires just one 2D reference image of the subject for performing a robust object detection, which is desirable when limited available storage onboard a spacecraft enforce constraints. Additionally, Sharif and Hölzel in a recent study illustrated ORB's robustness and invariance to orientation, rotation, and illumination variations. Thus, ORB is an ideal technique to guide a malfunctioning satellite that has no sense of orientation relative to its surroundings. ORB feature detector is a robust algorithm for detecting the subject when external factors are unpredictable, uncontrollable, and quickly changing. However, ORB is a 2D feature detector and unable to differentiate between surfaces of a subject. Via Bayesian probabilistic theorem, we proposed a new approach to help improve the confidence in detection.

Item URL in elib:https://elib.dlr.de/117266/
Document Type:Conference or Workshop Item (Speech)
Title:Robust 3D Object Detection
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pfaab, ChristianUniversität BremenUNSPECIFIEDUNSPECIFIED
Hölzel, MatthewUniversität BremenUNSPECIFIEDUNSPECIFIED
Date:June 2017
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Publisher:The European Space Agency (ESA)'s Automation and Robotics Department
Keywords:Bayesian probabilistic theorem, ORB feature detector, autonomous orientation detection.
Event Title:Advanced Space Technologies for Robotics and Automation (ASTRA)
Event Location:Leiden, The Netherlands
Event Type:international Conference
Event Dates:20-22 June 2017
Organizer:The European Space Agency (ESA)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Bremen
Institutes and Institutions:Institute of Space Systems > Navigation and Control Systems
Deposited By: Sharif, Helia
Deposited On:14 Dec 2017 10:19
Last Modified:14 Dec 2017 10:19

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