摘要
分析了便携式激光视觉扫描系统获取的点云数据存在的问题,针对具体问题分析了数据处理中的关键步骤和算法,使用手动剔除和系统判断相结合的方法,有效地剔除扫描数据中的噪声数据。同时,采用数据缩减算法实现对扫描点云的采样,在保证扫描曲面特征不失真的情况下,尽可能地缩减不必要的数据。数据经过处理后,不仅可以提高模型重构的精准度,更可以降低模型重构的复杂程度。
The characteristics of the point cloud data obtained by the portable laser vision scanning system is analyzed. Based on the distinct characteristics of the points, the critical steps and algorithms of the data processing are studied. Through the combination of the manual elimination method and system automatic identification method, the noise points of the point cloud data are removed effectively. At the same time, the data reduction algorithm is used to accomplish the sampling process of the scanning point cloud data. Under the promise of not to lose the surface characteristics, the unnecessary data are taken away as much as possible. Through the preprocessing, not only the accuracy of model reconstruction can be improved, but also the complexity of the model reconstruction can be reduced.
出处
《传感器与微系统》
CSCD
北大核心
2009年第11期55-57,60,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金重点资助项目(50735003)
关键词
视觉传感器
逆向工程
激光扫描
点云数据
数据处理
vision sensor
reverse engineering
laser scanning
point cloud data
data processing
