Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media
This paper presents a robust algorithm that is implemented for segmentation and characterization of traces obtained through a sweep process performed by a laser sensor. The process yields polar parameters that define segments of straight lines, which describe the scanning environment. A Mean-Shift C...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Spanish |
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Universidad Distrital Francisco Jose de Caldas
2013-09-01
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| Series: | Tecnura |
| Subjects: | |
| Online Access: | http://tecnura.udistrital.edu.co/ojs/index.php/revista/article/view/630/564 |
| _version_ | 1811273012212662272 |
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| author | Julie Stephany Berrío Lina María Paz Eduardo Caicedo Bravo |
| author_facet | Julie Stephany Berrío Lina María Paz Eduardo Caicedo Bravo |
| author_sort | Julie Stephany Berrío |
| collection | DOAJ |
| description | This paper presents a robust algorithm that is implemented for segmentation and characterization of traces obtained through a sweep process performed by a laser sensor. The process yields polar parameters that define segments of straight lines, which describe the scanning environment. A Mean-Shift Clustering strategy that uses the average of laser scanning points set in an orient-able ellipse is proposed as an estimate of the density gradient of points within the window. Grouping is achieved by sliding this ellipse into areas of space where the density of points is high, and it is redirected towards the direction of greater data dispersion. Each grouped set of points is processed by a modified RANSAC (Random Sample and Consensus) algorithm. This method involves the construction of model assumptions from minimal data subsets chosen at random and evaluates their validity supported by the whole of data, while the associated probability densities are updated. The parameters of the detected segments are estimated by a TLS (Total Least Squares) regression, which minimizes the sum of squared differences between the function and the data. The algorithm is evaluated in indoor environments using mobile robot platform Pioneer 3DX (equipped with a SICK laser sensor), obtaining satisfactory results in terms of compactness and error parameters of the lines detected. Likewise, tests were conducted using simulated data with constant density (where the classic MSC algorithm performs poorly), achieving significant improvements in segmentation and line parameterization. |
| first_indexed | 2024-04-12T22:51:32Z |
| format | Article |
| id | doaj.art-b4597a5dfe9c4accb93fd2166cc0f509 |
| institution | Directory Open Access Journal |
| issn | 0123-921X 2248-7638 |
| language | Spanish |
| last_indexed | 2024-04-12T22:51:32Z |
| publishDate | 2013-09-01 |
| publisher | Universidad Distrital Francisco Jose de Caldas |
| record_format | Article |
| series | Tecnura |
| spelling | doaj.art-b4597a5dfe9c4accb93fd2166cc0f5092022-12-22T03:13:20ZspaUniversidad Distrital Francisco Jose de CaldasTecnura0123-921X2248-76382013-09-0117378498Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de mediaJulie Stephany BerríoLina María PazEduardo Caicedo BravoThis paper presents a robust algorithm that is implemented for segmentation and characterization of traces obtained through a sweep process performed by a laser sensor. The process yields polar parameters that define segments of straight lines, which describe the scanning environment. A Mean-Shift Clustering strategy that uses the average of laser scanning points set in an orient-able ellipse is proposed as an estimate of the density gradient of points within the window. Grouping is achieved by sliding this ellipse into areas of space where the density of points is high, and it is redirected towards the direction of greater data dispersion. Each grouped set of points is processed by a modified RANSAC (Random Sample and Consensus) algorithm. This method involves the construction of model assumptions from minimal data subsets chosen at random and evaluates their validity supported by the whole of data, while the associated probability densities are updated. The parameters of the detected segments are estimated by a TLS (Total Least Squares) regression, which minimizes the sum of squared differences between the function and the data. The algorithm is evaluated in indoor environments using mobile robot platform Pioneer 3DX (equipped with a SICK laser sensor), obtaining satisfactory results in terms of compactness and error parameters of the lines detected. Likewise, tests were conducted using simulated data with constant density (where the classic MSC algorithm performs poorly), achieving significant improvements in segmentation and line parameterization.http://tecnura.udistrital.edu.co/ojs/index.php/revista/article/view/630/564principal component analysisregression analysismeasurement by laser beamclustering methods |
| spellingShingle | Julie Stephany Berrío Lina María Paz Eduardo Caicedo Bravo Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media Tecnura principal component analysis regression analysis measurement by laser beam clustering methods |
| title | Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media |
| title_full | Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media |
| title_fullStr | Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media |
| title_full_unstemmed | Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media |
| title_short | Segmentación y parametrización de líneas en datos láser 2D basado en agrupamiento por desplazamiento de media |
| title_sort | segmentacion y parametrizacion de lineas en datos laser 2d basado en agrupamiento por desplazamiento de media |
| topic | principal component analysis regression analysis measurement by laser beam clustering methods |
| url | http://tecnura.udistrital.edu.co/ojs/index.php/revista/article/view/630/564 |
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