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|Title: ||Autonomous metrology for robot mounted 3D vision systems|
|Authors: ||Kinnell, Peter|
Hodgson, John R.
Jackson, Michael R.
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|
|Publisher Link: ||https://doi.org/10.1016/j.cirp.2017.04.069|
|Appears in Collections:||Closed Access (Mechanical, Electrical and Manufacturing Engineering)|
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