研究目的
Recovering missing trajectory data for mobile laser scanning systems to ensure the data's availability for relevant models and algorithms.
研究成果
The proposed methods effectively recover the scanner's trajectory from MLS point cloud data with deviations of a few centimeters for the ground track and less than 1 decimeter for the scanner trajectory, making the data suitable for relevant algorithms.
研究不足
The method's effectiveness may be limited by the road's width, surface evenness, and the scanner's installation position. Narrow roads or uneven surfaces can affect the accuracy of the recovered trajectory.
1:Experimental Design and Method Selection:
The study proposes two approaches for recovering the scanner's trajectory from point cloud data based on different input conditions: ordered 3D coordinates with and without acquisition time. The methodology involves road point density analysis and plane reconstruction on a single scanning line.
2:Sample Selection and Data Sources:
Four typical urban, rural, winding, and viaduct road environments collected by two MLS systems from different manufacturers were used.
3:List of Experimental Equipment and Materials:
Mobile Laser Scanning (MLS) systems integrating 2D scanners with rotating mirror line sweeps.
4:Experimental Procedures and Operational Workflow:
The process includes extracting rough road points, filtering road points, analyzing point density to locate scanner ground tracks, refining the scanner's ground trajectory, and reconstructing the scanner trajectory.
5:Data Analysis Methods:
The study uses point density analysis, linear regression based on acquisition time, and line smoothing operations to refine the scanner's ground trajectory and reconstruct the scanner's position.
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