Robust Fitting of Geometric Primitives on LiDAR Data

Tekla Tóth, Levente Hajder

Abstract

This paper deals with robust surface fitting on spatial points measured by a LiDAR device. The point clouds contain hundreds of thousands data points. Therefore, the time demand of the algorithms is crucial for fast operation. We present two novel algorithms based on the RANSAC method: one for plane detection and one for other object detection. The execution time of the novel algorithms is significantly lower as only one random sampling is required because a deterministic teqnique selects the other data points. The accuracy of the novel methods are validated on synthesized data as well as real indoor and outdoor measurements.

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Paper Citation


in Harvard Style

Tóth T. and Hajder L. (2019). Robust Fitting of Geometric Primitives on LiDAR Data.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 622-629. DOI: 10.5220/0007572606220629


in Bibtex Style

@conference{visapp19,
author={Tekla Tóth and Levente Hajder},
title={Robust Fitting of Geometric Primitives on LiDAR Data},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={622-629},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007572606220629},
isbn={978-989-758-354-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Robust Fitting of Geometric Primitives on LiDAR Data
SN - 978-989-758-354-4
AU - Tóth T.
AU - Hajder L.
PY - 2019
SP - 622
EP - 629
DO - 10.5220/0007572606220629