Abstract
Abstract. Terrestrial laser scanning (TLS) systems have been established as a leading tool for the acquisition of high density three-dimensional point clouds from physical objects. The collected point clouds by these systems can be utilized for a wide spectrum of object extraction, modelling, and monitoring applications. Pole-like features are among the most important objects that can be extracted from TLS data especially those acquired in urban areas and industrial sites. However, these features cannot be completely extracted and modelled using a single TLS scan due to significant local point density variations and occlusions caused by the other objects. Therefore, multiple TLS scans from different perspectives should be integrated through a registration procedure to provide a complete coverage of the pole-like features in a scene. To date, different segmentation approaches have been proposed for the extraction of pole-like features from either single or multiple-registered TLS scans. These approaches do not consider the internal characteristics of a TLS point cloud (local point density variations and noise level in data) and usually suffer from computational inefficiency. To overcome these problems, two recently-developed PCA-based parameter-domain and spatial-domain approaches for the segmentation of pole-like features are introduced, in this paper. Moreover, the performance of the proposed segmentation approaches for the extraction of pole-like features from a single or multiple-registered TLS scans is investigated in this paper. The alignment of the utilized TLS scans is implemented using an Iterative Closest Projected Point (ICPP) registration procedure. Qualitative and quantitative evaluation of the extracted pole-like features from single and multiple-registered TLS scans, using both of the proposed segmentation approaches, is conducted to verify the extraction of more complete pole-like features using multipleregistered TLS scans.
Highlights
Over the past few years, Terrestrial Laser Scanning (TLS) systems − static or mobile − have been proven as an efficient and reliable technology for the acquisition of high density point clouds over physical surfaces
In order to overcome the limitations of the aforementioned techniques, this paper presents two recently-developed PCAbased parameter-domain and spatial-domain approaches for the segmentation and extraction of pole-like features from TLS scans
These datasets include single and registered multiple TLS scans – five overlapping TLS scans aligned to a common reference frame using the Iterative Closest Projected Point (ICPP) registration technique – which have been acquired in an electrical substation using a FARO Focus3D terrestrial laser scanner
Summary
Over the past few years, Terrestrial Laser Scanning (TLS) systems − static or mobile − have been proven as an efficient and reliable technology for the acquisition of high density point clouds over physical surfaces. Pole-like features are initially classified in the parameter domain and precisely modelled using a least-squares cylinder fitting procedure in the spatial domain (Bolles and Fischler, 1981; Chaperon and Goulette, 2001; Fischler and Bolles, 1981; Schnabel et al, 2007). In order to overcome the limitations of the aforementioned techniques, this paper presents two recently-developed PCAbased parameter-domain and spatial-domain approaches for the segmentation and extraction of pole-like features from TLS scans. These approaches are developed while considering the internal characteristics of the TLS scans. Selection of appropriate representation models for the classified pole-like features
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