Publication detail

Augmented Postprocessing of the FTLS Vectorization Algorithm

JELÍNEK, A. ŽALUD, L.

Original Title

Augmented Postprocessing of the FTLS Vectorization Algorithm

English Title

Augmented Postprocessing of the FTLS Vectorization Algorithm

Type

conference paper

Language

en

Original Abstract

Vectorization is a widely used technique in many areas, mainly in robotics and image processing. Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method. As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud. As a result, the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.

English abstract

Vectorization is a widely used technique in many areas, mainly in robotics and image processing. Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method. As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud. As a result, the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.

Keywords

Vectorization;Point Cloud;Linear Regression;Least Squares Fitting;Mobile Robotics

Released

29.07.2016

Location

Lisabon

ISBN

978-989-758-198-4

Book

Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2

Pages from

216

Pages to

223

Pages count

8

Documents

BibTex


@inproceedings{BUT127074,
  author="Aleš {Jelínek} and Luděk {Žalud}",
  title="Augmented Postprocessing of the FTLS Vectorization Algorithm",
  annote="Vectorization is a widely used technique in many areas,  mainly in robotics and image processing.   Applications in these domains frequently require both speed (for real-time operation) and accuracy (for maximal information gain). This paper proposes an optimization for the high speed vectorization methods, which leads to nearly optimal results. The FTLS algorithm uses the total least squares method for fitting the lines into the point cloud and the presented augmentation for the refinement of the results, is based on a modified Nelder-Mead method.  As shown on several experiments, this approach leads to better utilization of the information contained in the point cloud.  As a result,  the quality of approximation grows steadily with the number of points being vectorized, which was not achieved before. Performance costs are still comparable to the original algorithm, so the real-time operation is not endangered.",
  booktitle="Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 2",
  chapter="127074",
  doi="10.5220/0005962902160223",
  howpublished="print",
  year="2016",
  month="july",
  pages="216--223",
  type="conference paper"
}