Abstract
The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.
Highlights
The demand for accurate spatial data has been increasing rapidly in recent years
The second dataset is the point cloud captured near a supermarket near the Curtin University Bentley campus in Australia using the MDL Dynascan S250
Nguyen et al (2015) claimed that the robust segmentation based on Robust and Diagnostic PCA (RDPCA) fails in segmenting the sparse point clouds of this target
Summary
The demand for accurate spatial data has been increasing rapidly in recent years. Thanks to Global Navigation Satellite System, laser scanning, imaging and information technologies, mobile mapping technology has rapidly developed since the late 1980s. Point cloud segmentation is the process of grouping spatially connected points that have similar characteristics. Because planar features are dominate in manmade environments, they are widely used in point clouds registration and calibration processes (Chan et al, 2013; Rabbani et al, 2007). This paper focuses on the segmenting of planar features in MLS point clouds. The captured 3D point clouds of a MLS system (MLSs) that uses 2D laser scanner(s) as the imaging sensor(s) are derived by utilising the movement of the carrier vehicle. A scan profile or segment contains a group of points of the same scan line on the same surface. Point densities of a surface in the same point clouds can be defined as the distance between two adjacent scan profiles or profile spacing and the distance between two adjacent points of the same scan profile or point spacing (Figure 1)
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