Matlab Implementation Method of Point Cloud Elevation Normalization Processing

Matlab Implementation Method of Point Cloud Elevation Normalization Processing

Point cloud data is a collection of discrete points in three-dimensional space, commonly used in lidar, photogrammetry and other fields. The elevation information of point cloud data is of great significance to most application scenarios, so we need to normalize the elevation of point cloud. This article will introduce how to use Matlab to realize point cloud elevation normalization.

1. The principle of point cloud elevation normalization processing

The principle of point cloud elevation normalization processing is to scale the Z coordinate values ​​of all points according to a certain ratio by calculating the maximum and minimum values ​​of the Z coordinate values ​​of all points in the point cloud data, so that the Z coordinate values ​​of the point cloud data distributed over a relatively small area. The specific implementation process is as follows:

1. Calculate the maximum and minimum values ​​of Z coordinates in point cloud data.

2. Calculate the scaling ratio S = (Zmax - Zmin) / H, where H is the maximum value of the Z coordinate after elevation normalization.

3. The Z coordinate values ​​of all points in the point cloud data are scaled according to the following formula:

Znew = (Zold - Zmin) / S

After the above operation, the Z coordinate value of the point cloud data will be normalized to the range from 0 to H.

2. Matlab implements point cloud elevation normalization processing

In Matlab, we can use the following code to normalize the height of the point cloud:

% 读取点云数据
ptCloud = pcread('input.pcd');

% 计算 Z 坐标值的最大值和最小值
zmax = max(ptCloud.Location(:,3));
zmin = min(ptCloud.Location(:,3));

% 计算缩放比例
h = max(ptCloud.Location(:,3)) - min(ptCloud.Location(:,3));
S = (zmax - zmin) / h;

% 对点云数据进行高程归一化处理
ptCloud.Location(:,3) = (ptCloud.Location