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Method for 3D fibre reconstruction on a microrobotic platform
Author(s) -
HIRVONEN J.,
MYLLYS M.,
KALLIO P.
Publication year - 2016
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12370
Subject(s) - computer science , artificial intelligence , point (geometry) , sensitivity (control systems) , orientation (vector space) , computer vision , curvature , noise (video) , repeatability , sample (material) , iterative closest point , algorithm , mathematics , point cloud , geometry , image (mathematics) , physics , engineering , statistics , electronic engineering , thermodynamics
Summary Automated handling of a natural fibrous object requires a method for acquiring the three‐dimensional geometry of the object, because its dimensions cannot be known beforehand. This paper presents a method for calculating the three‐dimensional reconstruction of a paper fibre on a microrobotic platform that contains two microscope cameras. The method is based on detecting curvature changes in the fibre centreline, and using them as the corresponding points between the different views of the images. We test the developed method with four fibre samples and compare the results with the references measured with an X‐ray microtomography device. We rotate the samples through 16 different orientations on the platform and calculate the three‐dimensional reconstruction to test the repeatability of the algorithm and its sensitivity to the orientation of the sample. We also test the noise sensitivity of the algorithm, and record the mismatch rate of the correspondences provided. We use the iterative closest point algorithm to align the measured three‐dimensional reconstructions with the references. The average point‐to‐point distances between the reconstructed fibre centrelines and the references are 20–30 μm, and the mismatch rate is low. Given the manipulation tolerance, this shows that the method is well suited to automated fibre grasping. This has also been demonstrated with actual grasping experiments.