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Title: Object recognition and localisation from 3D point clouds by maximum likelihood estimation
Authors: Dantanarayana, Harshana G.
Huntley, Jonathan M.
Keywords: Fringe projection 3D scanning
Pose estimation
Object recognition
Industrial inspection
Issue Date: 2017
Publisher: The Royal Society
Citation: DANTANARAYANA, H.G. and HUNTLEY, J.M., 2017. Object recognition and localisation from 3D point clouds by maximum likelihood estimation. Royal Society Open Science, 4: 160693.
Abstract: We present an algorithm based on maximum likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’ based algorithms which normally discard such data. Compared to the 6D Hough transform it has negligible memory requirements, and is computationally efficient compared to iterative closest point (ICP) algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degree of freedom (DOF) example is given, followed by a full 6 DOF analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an rms alignment error as low as 0:3 mm.
Description: This is an Open Access Article. It is published by Royal Society under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: The research was funded by the Engineering and Physical Sciences Research Council under the Light Controlled Factory project EP/K018124/1.
Version: Published version
DOI: 10.1098/rsos.160693
URI: https://dspace.lboro.ac.uk/2134/26062
Publisher Link: https://doi.org/10.1098/rsos.160693
ISSN: 2054-5703
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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