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Open AccessArticle

Primitive Shape Fitting in Point Clouds Using the Bees Algorithm

Department of Mechanical Engineering, University of Birmingham, Birmingham B15 2TT, UK
Surrey Space Centre, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
Department of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5198;
Received: 16 September 2019 / Revised: 14 November 2019 / Accepted: 25 November 2019 / Published: 29 November 2019
(This article belongs to the Collection Advances in Automation and Robotics)
In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the bees algorithm obtained performances comparable to those obtained using the state-of-the-art random sample consensus (RANSAC) method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the bees algorithm over standard methods is that it doesn’t rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance. View Full-Text
Keywords: machine vision; primitive fitting; bees algorithm; optimisation machine vision; primitive fitting; bees algorithm; optimisation
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Baronti, L.; Alston, M.; Mavrakis, N.; Ghalamzan E., A.M.; Castellani, M. Primitive Shape Fitting in Point Clouds Using the Bees Algorithm. Appl. Sci. 2019, 9, 5198.

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