Feature detection with a constant FAR in sparse 3-D point cloud data
Author
dc.contributor.author
Lühr, Daniel
Author
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Adams, Martin
Author
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Houshiar, Hamidreza
Author
dc.contributor.author
Borrmann, Dorit
Author
dc.contributor.author
Nuechter, Andreas
Admission date
dc.date.accessioned
2020-05-06T19:37:53Z
Available date
dc.date.available
2020-05-06T19:37:53Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
IEEE Transactions on Geoscience and Remote Sensing, 58 (3): 1877-1891, 2020
es_ES
Identifier
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10.1109/TGRS.2019.2950292
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/174456
Abstract
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The detection of markers or reflectors within point cloud data (PCD) is often used for 3-D scan registration, mapping, and 3-D environmental modeling. However, the reliable detection of such artifacts is diminished when PCD is sparse and corrupted by detection and spatial errors, for example, when the sensing environment is contaminated by high dust levels, such as in mines. In the radar literature, constant false alarm rate (CFAR) processors provide solutions for extracting features within noisy data; however, their direct application to sparse, 3-D PCD is limited due to the difficulty in defining a suitable noise window. Therefore, in this article, CFAR detectors are derived, which are capable of processing a 2-D projected version of the 3-D PCD or which can directly process the 3-D PCD itself. Comparisons of their robustness, with respect to data sparsity, are made with various state-of-the-art feature detection methods, such as the Canny edge detector and random sampling consensus (RANSAC) shape detection methods.
es_ES
Patrocinador
dc.description.sponsorship
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
AFB180004
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT
1190979
CONICYT-Deutscher Akademischer Austauchdienst (DAAD), ChileGermany Collaborative Grant, through Automated 3D Scan Acquisition for fast Digitization of Mines
DAAD PCCI12009/56088171