Self-calibrated visual servoing with respect to axial-symmetric 3D objects

Fioravanti, Duccio; Colombo, Carlo; Allotta, Benedetto

doi:10.1016/j.robot.2008.05.006

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The modeling of projected SORs for pose estimation and reconstruction has been addressed as a single-view problem, where pose and camera calibration can be recovered either from bi-tangent points on the apparent contour [31], [29], imaged cross-sections detected as ellipses [4], [5], [10], or both bi-tangent points and cross-sections [3]. Methods have been proposed for the projective [27] and metric [5] reconstruction of an SOR from a single calibrated view when the pose in known, as well as image contours.…”

Section: Related Workmentioning

confidence: 99%

Seeing Glassware: from Edge Detection to Pose Estimation and Shape Recovery

Phillips¹,

Lecce²,

Daniilidis³

Robotics: Science and Systems XII

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Abstract-Perception of transparent objects has been an open challenge in robotics despite advances in sensors and datadriven learning approaches. In this paper, we introduce a new approach that combines recent advances in learnt object detectors with perceptual grouping in 2D, and projective geometry of apparent contours in 3D. We train a state of the art structured edge detector on an annotated set of foreground glassware. We assume that we deal with surfaces of revolution (SOR) and apply perceptual symmetry grouping in a 2D spherical transformation of the image to obtain a 2D detection of the glassware object and a hypothesis about its 2D axis. Rather than stopping at a single view detection, we ultimately want to reconstruct the 3D shape of the object and its 3D pose to allow for a robot to grasp it. Using two views allows us to decouple the 3D axis localization from the shape estimation. We develop a parametrization that uniquely relates the shape reconstruction of SOR to given a set of contour points and tangents. Finally, we provide the first annotated dataset for 2D detection, 3D pose and 3D shape of glassware and we show results comparable to category-based detection and localization of opaque objects without any training on the object shape.

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