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2021 | OriginalPaper | Buchkapitel

Searching the Optimal Parameters of a 3D Scanner Through Particle Swarm Optimization

verfasst von : João Braun, José Lima, Ana I. Pereira, Cláudia Rocha, Paulo Costa

Erschienen in: Optimization, Learning Algorithms and Applications

Verlag: Springer International Publishing

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Abstract

The recent growth in the use of 3D printers by independent users has contributed to a rise in interest in 3D scanners. Current 3D scanning solutions are commonly expensive due to the inherent complexity of the process. A previously proposed low-cost scanner disregarded uncertainties intrinsic to the system, associated with the measurements, such as angles and offsets. This work considers an approach to estimate these optimal values that minimize the error during the acquisition. The Particle Swarm Optimization algorithm was used to obtain the parameters to optimally fit the final point cloud to the surfaces. Three tests were performed where the Particle Swarm Optimization successfully converged to zero, generating the optimal parameters, validating the proposed methodology.

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Titel: Searching the Optimal Parameters of a 3D Scanner Through Particle Swarm Optimization
verfasst von: João Braun
José Lima
Ana I. Pereira
Cláudia Rocha
Paulo Costa
Verlag: Springer International Publishing
Buch: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_11

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