Zusammenfassung
In diesem Beitrag wird ein Design für eine visuelle Passermarke zur Posenschätzung von Kameras vorgestellt, die sogenannte Dynamische Passermarke. Die Passermarke wird auf einem Anzeigegerät dargestellt und passt ihr Aussehen an die räumlichen und zeitlichen Anforderungen der visuellen Wahrnehmungsaufgabe an. Dies ist vor allem für mobile Roboter von Vorteil, die eine Kamera als Sensor verwenden. Es wird eine Regelung entworfen, die das Aussehen der Passermarke in Abhängigkeit der aktuellen Pose der Kamera verändert, um den Erfassungsbereich der Kamera für die Posenschätzung zu vergrößern und eine höhere Genauigkeit der Posenschätzung im Vergleich zu herkömmlichen passiven Passermarken zu erreichen.
Abstract
This paper introduces a visual fiducial marker design, the dynamic fiducial marker for machine vision applications. With this new fiducial, the estimation of the pose of a calibrated camera can be improved via the dynamic adjustment of the fiducial displayed on a display device. The dynamic fiducial marker can change its appearance according to the spatiotemporal requirements of the visual perception task, especially in the case of a mobile robot using a camera as a sensor. We present a feedback control scheme that changes the appearance of the marker depending on the current pose of the camera, in order to increase the range of detection for pose estimation and to to achieve higher accuracy of pose estimation compared to common passive fiducial markers.
Dieser Artikel ist dem 60. Geburtstag von Prof. Dr.-Ing. Jürgen Adamy gewidmet.
Über die Autoren
Dr.-Ing. Raul Acuna hat Elektrotechnik und Mechatronik an der Simón Bolivar Universität studiert und an der TU Darmstadt im Fachgebiet Regelungsmethoden und Robotik promoviert. Momentan arbeitet er als Computer Vision Engineer bei Hyundai MOBIS, Hauptarbeitsgebiete: Maschinelles Sehen, Kamera Kalibrierung, Mobile Robotik.
Prof. Dr.-Ing. Volker Willert ist Professor für maschinelles Sehen der Fakultät Elektrotechnik der Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt. Hauptarbeitsgebiete: Maschinelles Sehen, Mobile Robotik, Maschinelles Lernen, Multi-Agenten-Systeme.
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