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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24998

Title: Autonomous metrology for robot mounted 3D vision systems
Authors: Kinnell, Peter
Rymer, Tom
Hodgson, John R.
Justham, Laura
Jackson, Michael R.
Keywords: Metrology
Cognitive robotics
3D image processing
Issue Date: 2017
Publisher: © Elsevier on behalf of CIRP
Citation: KINNELL, P. ...et al., 2017. Autonomous metrology for robot mounted 3D vision systems. CIRP Annals - Manufacturing Technology, 66(1), pp.483-486.
Abstract: Using a metrology system simulation approach, an algorithm is presented to determine the best position for a robot mounted 3D vision system. Point cloud data is simulated, taking into account sensor performance, to create a ranked list of the best camera positions. These can be used by a robot to autonomously determine the most advantageous camera position for locating a target object. The algorithm is applied to an Ensenso active stereo 3D camera. Results show that when used in combination with a RANSAC object recognition algorithm, it increased positional precision by two orders of magnitude, from worst to best case.
Description: This work is in closed access until 29th Nov 2018
Sponsor: This work was funded by UK Engineering and Physical Research Council (grants EP/L01498X/1 and EP/I033467/1).
Version: Accepted for publication
DOI: 10.1016/j.cirp.2017.04.069
URI: https://dspace.lboro.ac.uk/2134/24998
Publisher Link: https://doi.org/10.1016/j.cirp.2017.04.069
ISSN: 0007-8506
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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