Point Cloud Process of Laser Scanning with a Mathematical Noise Model

Abstract:

Considering the unreasonable noise of laser scanning point data during measuring, which causes the reconstructed curve and surface rough, some problems are analyzed to deal with noise. The mathematical noise model on scanning point cloud is proposed, which consists of deterministic and random noise error, and the random error mainly consists of geometric error and measurement one. The data process is proposed to reduce noise and to simplify data. The noise process consists of obvious noise removed, random filter algorithm and data fairing with an optimized correction value. Due to redundant data about mass point cloud, method of combining deviation parameters with allowed angle is proposed to simplify point cloud. The experiments show that the proposed method has many obvious advantages than a single algorithm of data processing, such as all sorts of noise data can be removed respectively, mass data can be streamlined. And the proposed algorithm has the advantages of high reservation of curve and surface reconstruction of point cloud.

• Periodical: Applied Mechanics and Materials (Volumes 275-277)
• Pages: 2537-2542
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.275-277.2537
• Online since: January 2013
• Authors: Na Meng, Jian Zhong Zhang, Bao Qing Dai, Xin Li Chen
• Keywords: Deterministic Error, Deviation Parameters, Fairing with Correction Value, Maximum Allowed Angle, Noise Model, Random Noise

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