Abstract
Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.
Highlights
The three-dimensional (3D) information of roads and ancillary facilities is the basic content of city information construction
This paper proposed a complete method for pole-like objects (PLOs) recognition
The PLOs were extracted according to the independence analysis and cylindrical or linear feature detection
Summary
The three-dimensional (3D) information of roads and ancillary facilities is the basic content of city information construction. The high-precision 3D information of roads and ancillary facilities plays an important role in road safety inspection, road facility management, maintenance and 3D city modeling [1,2,3,4]. The increase of new roads and road reconstructions has resulted in a rapid renewal in the number and type of PLOs. The traditional manual methods cannot accommodate the collection of massive 3D information of PLOs. The traditional manual methods cannot accommodate the collection of massive 3D information of PLOs It is time consuming and increases the overall cost when managing, maintaining and updating the basic data as to road and ancillary facilities data
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