Abstract
The paper proposes a method supported by MATLAB for detection and measurement of missing point regions (MPR) which may cause severe road information loss in mobile laser scanning (MLS) point clouds. First, the scan-angle thresholds are used to segment the road area for MPR detection. Second, the segmented part is mapped onto a binary image with a pixel size of ε through rasterization. Then, MPR featuring connected 1-pixels are identified and measured via image processing techniques. Finally, the parameters regarding MPR in the image space are reparametrized in relation to the vehicle path recorded in MLS data for a better understanding of MPR properties on the geodetic plane. Tests on two MLS datasets show that the output of the proposed approach can effectively detect and assess MPR in the dataset. The ε parameter exerts a substantial influence on the performance of the method, and it is recommended that its value should be optimized for accurate MPR detections.
Highlights
Mobile light detection and ranging (LiDAR) systems, known as Mobile Mapping Systems (MMSs), are an emerging survey technology that enable quick and accurate depiction of real-world three-dimensional (3D) road environments in the form of dense point clouds
Mobile LiDAR System The mobile LiDAR system used for collecting point cloud data is the Hi-Scan MMS from
This paper has proposed a solution for automated detection and measurement of road’s missing point regions in mobile laser scanning data
Summary
Mobile light detection and ranging (LiDAR) systems, known as Mobile Mapping Systems (MMSs), are an emerging survey technology that enable quick and accurate depiction of real-world three-dimensional (3D) road environments in the form of dense point clouds. The LiDAR comprises the Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and laser scanning system Due to their realistic representation of real-life objects, mobile laser scanning (MLS) data provide an ideal virtual environment for various computerized estimations, in which dangerous and cumbersome field measurements, such as sight distance [1], can be avoided. Rich information, such as intensity, scan angle, and trajectory of the inspection vehicle, is included in MLS data, which aids the recognition and segmentation of different objects. Though the specific objective of each category is different, the general concept of using MLS data is similar: that is, to automatically extract certain road information whose measurement is costly, time-consuming, and labor-intensive in the real-world.
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