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Autonomous metrology for robot mounted 3D vision systems
journal contribution
posted on 2017-05-15, 13:20 authored by Peter KinnellPeter Kinnell, Tom Rymer, John R. Hodgson, Laura JusthamLaura Justham, Michael JacksonUsing 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.
Funding
This work was funded by UK Engineering and Physical Research Council (grants EP/L01498X/1 and EP/I033467/1).
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
CIRP Annals - Manufacturing TechnologyVolume
66Issue
1Citation
KINNELL, P. ...et al., 2017. Autonomous metrology for robot mounted 3D vision systems. CIRP Annals - Manufacturing Technology, 66(1), pp.483-486.Publisher
© Elsevier on behalf of CIRPVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-04-07Publication date
2017-04-29Notes
This work was published in the journal CIRP Annals - Manufacturing Technology and the definitive published version is available at https://doi.org/10.1016/j.cirp.2017.04.069.ISSN
0007-8506Publisher version
Language
- en