一种点云数据噪声点的随机滤波处理方法

A Random Filter Algorithm for Reducing Noise Error of Point Cloud Data

目前逆向工程中广泛采用激光扫描法来获取数据 ,测量过程中不可避免地混有不合理的噪声点 ,导致重构的曲线、曲面不光滑 ,因此 ,需要去除数据中的噪声点。对激光线扫描法获取数据的噪声点处理方法进行了研究。噪声点处理方法与点云数据的排列形式有关 ,通过对点云数据噪声数学模型的分析 ,认为激光线扫描法获取数据时 ,噪声点的产生主要是由随机误差引起的 ,其特点是幅值大 ,在光刀扫描线上引起较大的尖峰 ,据此提出一种简单、快速、实用的降噪方法——随机滤波法。该方法通过比较连续点之间的相对位置 ,给定一个阈值 ,将其中位置起伏较大的点判定为噪声点并予以去除。通过实例验证该方法能满足曲线。

Measured data are obtained through a laser scanner in reverse engineering. The real data inevitably contain unreasonably noise error during measuring. The noise error causes the reconstructed curve and surface rough. Therefore it is essential to remove the noise error. This paper investigates the method on reducing noise error of the measured data obtained through laser line scanning. The method on reducing noise error is closely related to the organization of the point cloud data. This paper analyzes the mathematical model about the point cloud data error. The noise error is mainly caused by random error. The characteristic of noise error is that the swing value is bigger and the peak arises on the scanning line. According to this feature, a method named the random filter algorithm is put forward for reducing noise error, and it is simple, quick and practical. The procedure of this algorithm is first to compare the relative position among the successive points. Then the points that their positions oscillate bigger are judged noise error according to a threshold and will be removed. The principle and the step are described in detail, and it is proved by some examples that the processing result of the method is effective and can meet the requirements of curve and surface reconstruction.