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Title: Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features
Authors: Dantanarayana, Harshana G.
Huntley, Jonathan M.
Keywords: Pose estimation
Maximum likelihood
Curve features
Edge features
Surface features
Point clouds
Issue Date: 2017
Publisher: © SPIE
Citation: DANTANARAYANA, H.G. and HUNTLEY, J.M., 2017. Improved maximum likelihood estimation of object pose from 3D point clouds using curves as features. Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340D (June 26, 2017); doi:10.1117/12.2270197.
Series/Report no.: Proceedings of SPIE;10334
Abstract: Object recognition and pose estimation is a fundamental problem in automated quality control and assembly in the manufacturing industry. Real world objects present in a manufacturing engineering setting tend to contain more smooth surfaces and edges than unique key points, making state-of-the-art algorithms that are mainly based on key-point detection, and key-point description with RANSAC and Hough based correspondence aggregators, unsuitable. An alternative approach using maximum likelihood has recently been proposed in which surface patches are regarded as the features of interest1. In the current study, the results of extending this algorithm to include curved features are presented. The proposed algorithm that combines both surfaces and curves improved the pose estimation by a factor up to 3×, compared to surfaces alone, and reduced the overall misalignment error down to 0.61 mm.
Description: Copyright 2017 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Sponsor: The research was funded by the Engineering and Physical Sciences Research Council under the Light Controlled Factory project EP/K018124/1.
Version: Published
DOI: 10.1117/12.2270197
URI: https://dspace.lboro.ac.uk/2134/25981
Publisher Link: http://dx.doi.org/10.1117/12.2270197
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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