City Modelling, Intelligent Transportation, Saliency Feature, Mobile Mapping, Road Safety, Robust Statistics
Abstract :
[en] Pole-like object (PLO) detection and segmentation are important in many applications, such as 3D city modelling, urban planning, road assets monitoring, intelligent transportation, road safety, and forest monitoring. Arguably, vehicle-based mobile laser scanning (MLS) is the best on-road data acquisition system, because it is fast, precise and non-invasive. As part of that, laser scanning georeferenced data (i.e., point clouds) provide detailed structural morphology of the scanned objects. However, point clouds are not free from outliers and noise. Critically, many of the object extraction methods that depend on local saliency features (e.g., normals)-based segmentation use principal component analysis (PCA). PCA can provide the local features but struggle to produce robust results in the presence of outliers and noise. To reduce the influence of outliers for saliency features estimation and in segmentation, this paper employs Robust distance-based Diagnostic PCA (RD-PCA) coupled with the well-known DBSCAN clustering algorithm. This study contributes to a better understanding of object detection and segmentation by (i) exploring the problems of local saliency features estimation in the presence of outliers and noise; (ii) understanding problems with PCA and why RD-PCA is important; and (iii) introducing a novel method for PLOs detection and segmentation following a robust segmentation approach. The performance of the new algorithm is demonstrated through MLS data acquired in an urban road setup.
Disciplines :
Civil engineering
Author, co-author :
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) ; Unilu - University of Luxembourg [LU] > Institute for Advanced Stusdies (IAS)
Sadahiro, Yukio; University of Tokyo, Japan > 3 Interfaculty Initiative in Information Studies
TEFERLE, Felix Norman ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Laefer, Debra; NYU - New York University [US-NY] > Center for Urban Science and Progress and Department of Civil and Urban Engineering
Li, Jonathan; University of Waterloo > Geography and Environmental Management
External co-authors :
yes
Language :
English
Title :
DETECTION AND SEGMENTATION OF POLE-LIKE OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS
Publication date :
2023
Event name :
ISPRS Geospatial Week, 2023
Event organizer :
ISPRS
Event place :
Cairo, Egypt
Event date :
2-7, September, 2023
Audience :
International
Main work title :
DETECTION AND SEGMENTATION OF POLE-LIKE OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS
Publisher :
ISPRS, Cairo, Egypt
Peer reviewed :
Peer reviewed
Funders :
Institute for Advanced Studies (IAS), University of Luxembourg
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