elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

Environment- and Self-Modeling through Camera-Based Pose Estimation

Nissler, Christian (2019) Environment- and Self-Modeling through Camera-Based Pose Estimation. Fortschritte der Robotik / Progress in Robotics, 2. Shaker Verlag. ISBN 978-3-8440-7048-4.

[img] PDF - Nur DLR-intern zugänglich
18MB

Offizielle URL: http://www.shaker.de/shop/978-3-8440-7048-4

Kurzfassung

Environments, in which robots can assist humans both in production tasks as well as in everyday tasks, will demand advanced capabilities of these robotic systems of cooperating with humans and other robots. To achieve this, robots should be able to navigate and manipulate in dynamic environments safely. As such, it is essential that a robot can accurately determine its pose (i.e., its position and orientation) in the environment based on optical sensors. However, both the map of the robot's surrounding as well as its sensors can contain inaccuracies, which can cause problematic consequences. The work presented here focuses on this issue by introducing several novel computer vision-based methods. These approaches lead to a set of challenges which are addressed in this book. These are: How accurately can a robot estimate its pose in a known environment, i.e., assuming that a precise map of its surrounding is available? Secondly, how can a model of the robot's surroundings be created if no map of its surroundings is known a priori? Lastly, how can this be done if neither a priori environment models nor models of the robot's internal state are available? The introduced methods are experimentally evaluated throughout this book employing different mobile robotic systems, ranging from industrial manipulators to humanoid robots. Going beyond traditional robotics, this work examines how the presented methods can also be applied to human-machine interaction. It shows, that by solely visually observing the movement of the muscles in the human forearm and by employing machine learning methods, the corresponding hand gestures can be determined, opening entirely new possibilities in the control of robotic hands and hand prostheses.

elib-URL des Eintrags:https://elib.dlr.de/133643/
Dokumentart:Lehr- oder Fachbuch
Titel:Environment- and Self-Modeling through Camera-Based Pose Estimation
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Nissler, ChristianChristian.Nissler (at) dlr.dehttps://orcid.org/0000-0003-4361-9041NICHT SPEZIFIZIERT
Datum:4 Dezember 2019
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Band:2
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Thomas, Ulrikeulrike.thomas (at) etit.tu-chemnitz.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Verlag:Shaker Verlag
Name der Reihe:Fortschritte der Robotik / Progress in Robotics
ISBN:978-3-8440-7048-4
Status:veröffentlicht
Stichwörter:Pose Estimation; Calibration; Camera-Camera Calibration; Localization; Hand-Eye Calibration
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben Multisensorielle Weltmodellierung (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition
Hinterlegt von: Nissler, Christian
Hinterlegt am:20 Jan 2020 18:59
Letzte Änderung:29 Mär 2023 11:36

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.