A novel hybrid approach for the extraction of linear/cylindrical features from laser scanning data
Other literature type
(issn: 2194-9050, eissn: 2194-9050)
However, the collected point cloud should undergo manipulation approaches to be utilized for diverse civil, industrial, and military
applications. Different processing techniques have consequently been implemented for the extraction of low-level features from this
data. Linear/cylindrical features are among the most important primitives that could be extracted from laser scanning data, especially
those collected in industrial sites and urban areas. This paper presents a novel approach for the identification, parameterization, and
segmentation of these features in a laser point cloud. In the first step of the proposed approach, the points which belong to
linear/cylindrical features are detected and their appropriate representation models are chosen based on the principal component
analysis of their local neighborhood. The approximate direction and position parameters of the identified linear/cylindrical features
are then refined using an iterative line/cylinder fitting procedure. A parameter-domain segmentation method is finally applied to
isolate the points which belong to individual linear/cylindrical features in direction and position attribute spaces, respectively.
Experimental results from real datasets will demonstrate the feasibility of the proposed approach for the extraction of
linear/cylindrical features from laser scanning data.