<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Non-contact measurement techniques carried out using triangulation optical sensors are increasingly popular in measurements with the use of industrial robots directly on production lines. The result of such measurements is often a cloud of measurement points that is characterized by considerable measuring noise, presence of a number of points that differ from the reference model, and excessive errors that must be eliminated from the analysis. To obtain vector information points contained in the cloud that describe reference models, the data obtained during a measurement should be subjected to appropriate processing operations. The present paperwork presents an analysis of suitability of methods known as RANdom Sample Consensus (RANSAC), Monte Carlo Method (MCM), and Particle Swarm Optimization (PSO) for the extraction of the reference model. The effectiveness of the tested methods is illustrated by examples of measurement of the height of an object and the angle of a plane, which were made on the basis of experiments carried out at workshop conditions.</p></abstract>

Non-contact measurement techniques carried out using triangulation optical sensors are increasingly popular in measurements with the use of industrial robots directly on production lines. The result of such measurements is often a cloud of measurement points that is characterized by considerable measuring noise, presence of a number of points that differ from the reference model, and excessive errors that must be eliminated from the analysis. To obtain vector information points contained in the cloud that describe reference models, the data obtained during a measurement should be subjected to appropriate processing operations. The present paperwork presents an analysis of suitability of methods known as RANdom Sample Consensus (RANSAC), Monte Carlo Method (MCM), and Particle Swarm Optimization (PSO) for the extraction of the reference model. The effectiveness of the tested methods is illustrated by examples of measurement of the height of an object and the angle of a plane, which were made on the basis of experiments carried out at workshop conditions.

Alternative Methods for Estimating Plane Parameters Based on a Point Cloud

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Alternative Methods for Estimating Plane Parameters Based on a Point Cloud"}

Non-contact measurement techniques carried out using triangulation optical sensors are increasingly popular in measurements with the use of industrial robots...

Alternative Methods for Estimating Plane Parameters Based on a Point Cloud

Pubblicato online: 22 nov 2017

Pagine: 282 - 289

Ricevuto: 26 ago 2017

Accettato: 13 nov 2017

DOI: https://doi.org/10.1515/msr-2017-0035

Parole chiave
Robotic inspection, plane detection, Particle Swarm Optimization, RANSAC, Monte Carlo Method

© by Roman Stryczek

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Alternative Methods for Estimating Plane Parameters Based on a Point Cloud

Pubblicato online: 22 nov 2017

Pagine: 282 - 289

Ricevuto: 26 ago 2017

Accettato: 13 nov 2017

DOI: https://doi.org/10.1515/msr-2017-0035

Parole chiaveRobotic inspection, plane detection, Particle Swarm Optimization, RANSAC, Monte Carlo Method

© by Roman Stryczek

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
Robotic inspection, plane detection, Particle Swarm Optimization, RANSAC, Monte Carlo Method