From 5135038d10ffcb7bf8626fd3ddc7c302f87a9b85 Mon Sep 17 00:00:00 2001 From: Robert Jeutter Date: Wed, 3 Nov 2021 15:28:14 +0100 Subject: [PATCH] adding worksheet --- Proseminar.bib | 123 +++++++++++++++++++++++++++++++++++++++++++++++++ Proseminar.pdf | Bin 0 -> 244775 bytes Proseminar.tex | 102 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 225 insertions(+) create mode 100644 Proseminar.bib create mode 100644 Proseminar.pdf create mode 100644 Proseminar.tex diff --git a/Proseminar.bib b/Proseminar.bib new file mode 100644 index 0000000..3b713f2 --- /dev/null +++ b/Proseminar.bib @@ -0,0 +1,123 @@ +% Encoding: UTF-8 +@Comment{jabref-meta: databaseType:bibtex;} + +@misc{6dofGrasping, + TITLE = {6-DOF Grasping for Target-driven Object Manipulation in Clutter}, + AUTHOR = {Adithyavairavan Murali and Arsalan Mousavian and Clemens Eppner and Chris Paxton and Dieter Fox}, + YEAR = {2020}, + EPRINT = {1912.03628}, + ARCHIVEPREFIX = {arXiv}, + URL = {arxiv.org/abs/1912.03628}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{6pack, + TITLE = {6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints}, + AUTHOR = {Chen Wang and Roberto Martín-Martín and Danfei Xu and Jun Lv and Cewu Lu and Li Fei-Fei and Silvio Savarese and Yuke Zhu}, + YEAR = {2019}, + EPRINT = {1910.10750}, + ARCHIVEPREFIX = {arXiv}, + URL = {arxiv.org/abs/1910.10750}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{BundleTrack, + TITLE = {BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models}, + AUTHOR = {Bowen Wen and Kostas Bekris}, + YEAR = {2021}, + EPRINT = {2108.00516}, + ARCHIVEPREFIX = {arXiv}, + HOWPUBLISHED = {Website}, + URL = {arxiv.org/abs/2108.00516}, + NOTE = {Aufgerufen 23.10.2021} +} + +@article{CategoryLevelMetric, + TITLE = {Category-Level Metric Scale Object Shape and Pose Estimation}, + VOLUME = {6}, + ISSN = {2377-3774}, + URL = {dx.doi.org/10.1109/LRA.2021.3110538}, + DOI = {10.1109/lra.2021.3110538}, + NUMBER = {4}, + JOURNAL = {IEEE Robotics and Automation Letters}, + PUBLISHER = {Institute of Electrical and Electronics Engineers (IEEE)}, + AUTHOR = {Taeyeop Lee and Byeong-Uk Lee and Myungchul Kim and I.S Kweon}, + YEAR = {2021}, + MONTH = {Oct}, + PAGES = {8575–8582}, + URL = {arxiv.org/abs/2109.00326}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{CategoryLevelObject, + TITLE = {Category Level Object Pose Estimation via Neural Analysis-by-Synthesis}, + AUTHOR = {Xu Chen and Zijian Dong and Jie Song and Andreas Geiger and Otmar Hilliges}, + YEAR = {2020}, + EPRINT = {2008.08145}, + ARCHIVEPREFIX = {arXiv}, + URL = {arxiv.org/abs/2008.08145}, + NOTE = {Aufgerufen 27.10.2021} +} + +@article{Deepim, + TITLE = {DeepIM: Deep Iterative Matching for 6D Pose Estimation}, + VOLUME = {128}, + ISSN = {1573-1405}, + URL = {dx.doi.org/10.1007/s11263-019-01250-9}, + URL = {arxiv.org/abs/1804.00175}, + DOI = {10.1007/s11263-019-01250-9}, + NUMBER = {3}, + JOURNAL = {International Journal of Computer Vision}, + PUBLISHER = {Springer Science and Business Media LLC}, + AUTHOR = {Yi Li and Gu Wang and Xiangyang Ji and Yu Xiang and Dieter Fox}, + YEAR = {2018}, + MONTH = {11}, + PAGES = {657–678}, + NOTE = {Aufgerufen 16.10.2021} +} + +@article{GaussianFilter, + TITLE = {Depth-based object tracking using a Robust Gaussian Filter}, + URL = {dx.doi.org/10.1109/ICRA.2016.7487184}, + DOI = {10.1109/icra.2016.7487184}, + JOURNAL = {2016 IEEE International Conference on Robotics and Automation (ICRA)}, + PUBLISHER = {IEEE}, + AUTHOR = {Jan Issac and Manuel Wuthrich and Cristina Cifuentes and Jeanette Bohg and Sebastian Trimpe and Stefan Schaal}, + YEAR = {2016}, + MONTH = {3}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{MaskFusion, + TITLE = {MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects}, + AUTHOR = {Martin Rünz and Maud Buffier and Lourdes Agapito}, + YEAR = {2018}, + EPRINT = {1804.09194}, + ARCHIVEPREFIX = {arXiv}, + URL = {arxiv.org/abs/1804.09194}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{NormalizedObjectCoordiante, + TITLE = {Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation}, + AUTHOR = {He Wang and Srinath Sridhar and Jingwei Huang and Julien Valentin and Shuran Song and Leonidas J. Guibas}, + YEAR = {2019}, + EPRINT = {1901.02970}, + ARCHIVEPREFIX = {arXiv}, + URL = {arxiv.org/abs/1901.02970}, + NOTE = {Aufgerufen 27.10.2021} +} + +@misc{PoseCNN, + TITLE = {PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes}, + AUTHOR = {Yu Xiang and Tanner Schmidt and Venkatraman Narayanan and Dieter Fox}, + YEAR = {2017}, + EPRINT = {1711.00199}, + ARCHIVEPREFIX = {arXiv}, + HOWPUBLISHED = {Website}, + URL = {arxiv.org/abs/1711.00199}, + NOTE = {Aufgerufen 16.10.2021} +} + + +\endinput \ No newline at end of file diff --git a/Proseminar.pdf b/Proseminar.pdf new file mode 100644 index 0000000000000000000000000000000000000000..81746f045183688e65ea1991e3cf6b8b2edb31f9 GIT binary patch literal 244775 zcma&LLy#^?&@KA4ZQHi3-L|#6ciXmY+qP}nwr$&d=i$D|Ke)FBE3zUZa+DPdMW!Gk zM#n_Y22D1%G`t4Q&P>QiXlq~r&C3hTAY*J}>SRX9#L3D*`2QSe1~GFhCu0Xf1~Dsr zCu0#~Lt7(bXnuZZM<)kkeQRj9jc5%mbsP>vpV`{GLNC2m&)z4vgOE9GX^Dlz1qiMb z>t)Y 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+\setlength{\parindent}{0pt} + +\usepackage[style=alphabetic]{biblatex} +\setlength{\bibitemsep}{1em} +\bibliography{Proseminar} + +\usepackage{glossaries} +\setglossarystyle{list} +\makeglossaries +\newglossaryentry{cnn}{ + name=Convolutional Neural Network, + description={Besitzt pro Convolutional Layer mehrere Filterkerne, sodass Schichten an Feature Maps entstehen, die jeweils die gleiche Eingabe bekommen, jedoch aufgrund unterschiedlicher Gewichtsmatrizen unterschiedliche Features extrahieren.} +} +\newglossaryentry{quaternion}{ + name={Quaternion-Darstellung}, + description={Darstellung $V$ einer Gruppe $G$, die einen $G$-invarianten Homomorphismus $J:V\rightarrow V$ besitzt, der antilinear ist und $J^2=-Id$ erfüllt.} + } + +\title{Proseminar Objektposenschätzung} +\author{Robert Jeutter} +\date{\today} +\pdfinfo{ + /Title (Proseminar Objektposenschätzung) + /Creator (TeX) + /Producer (pdfTeX 1.40.0) + /Author (Robert Jeutter) + /Subject (Deeplearning, Robotische Manipulation) +} + +\begin{document} +\maketitle + +\begin{multicols*}{2} + \textbf{Zur Interaktion mit seiner Umwelt muss ein Roboter die Lage der Objekte in seiner Umgebung erkennen. Der Artikel schafft einen Überblick über aktuelle Verfahren mit Fokus auf Verfahren die keine Objektmodelle benötigen. } + %Damit ein Roboter einen Gegenstand greifen kann, ist es meist notwendig die genaue Lage des Objektes zu kennen. Dies kann sowohl über klassische Verfahren als auch über Deep-Learning-Verfahren erreicht werden. Ziel dieses Hauptseminars ist es den Stand der Technik für die Objektposenschätzung aufzuarbeiten und vorzustellen. Der Fokus sollte dabei auf Verfahren liegen, bei denen zuvor kein Objektmodell benötig wird, sodass auch die Lage von unbekannten Objekten geschätzt werden kann. + + \section{Motivation} + Roboter in Industrie und Assistenz treffen häufig auf nicht vorhersehbare oder vorab programmierbare Herausforderungen. Damit + Die Schätzung der 6D-Position bekannter Objekte ist wichtig für die Interaktion von Robotern mit der realen Welt wichtig. Das Problem ist aufgrund der Vielfalt der Objekte sowie der Komplexität einer Szene, die durch Unordnung und Verdeckungen zwischen den Objekten verursacht wird, eine Herausforderung. + + \section{Anforderungen} + Objektmodelle, schablonenbasierte Methoden und merkmalsbasierte Methoden + + Hardware-Ausstattung: 2D/3D Kamera, RGB-D, positionsveränderung der Kamera + + Verarbeitung: Komplexität, Geschwindigkeit, Genauigkeit + + Verkettete Verarbeitung: verarbeitung eines Bilderstroms statt einzelner Bilder für sich + + \section{Verschiedene Verfahren} + \subsection{BundleTrack\cite{BundleTrack}} + + \subsection{DeepIM\cite{Deepim}} + + \subsection{MaskFusion\cite{MaskFusion}} + + \subsection{Neural Analysis-by-Synthesis\cite{CategoryLevelObject}} + + \subsection{6-PACK\cite{6pack}} + + \subsection{PoseCNN\cite{PoseCNN}} + neues \Gls{cnn} für die 6D-Objektposenschätzung. PoseCNN schätzt die 3D-Verschiebung eines Objekts, indem es sein Zentrum im Bild lokalisiert und seinen Abstand zur Kamera vorhersagt. Die 3D-Rotation des Objekts wird durch Regression auf eine \Gls{quaternion} geschätzt. Dabei führt man eine neue Verlustfunktion ein, die es PoseCNN ermöglicht, symmetrische Objekte zu behandeln. + + \subsection{Robust Gaussian Filter\cite{GaussianFilter}} + + \section{Vergleich verschiedener Verfahren} + + \section{Fazit} + +\end{multicols*} + +\medskip + +\printglossary[title=Glossar] + +\printbibliography[title=Literatur] + +\end{document} \ No newline at end of file