Median filtering in PCL point clouds
Overview:
Point cloud data is composed of a large number of 3D points, and it is widely used in computer vision, robotics, and autonomous driving. However, point cloud data usually contains some outliers due to issues such as sensor noise, occlusion, and incomplete data. To remove these outliers and smooth the data, median filtering is widely used in point cloud processing.
Median filtering principle:
Median filtering is a nonlinear filtering method that uses the median value in the pixel neighborhood to replace the value of the current pixel. In the point cloud, the neighborhood around each point can also be regarded as a pixel neighborhood. The purpose of performing median filtering on each point is to remove the influence of abnormal points, maintain the clarity of edge information, and maintain the point cloud shape features.
Implement median filtering in PCL:
PCL (Point Cloud Library) is an open source point cloud library that provides many point cloud processing functions. Here is an example code for median filtering using the PCL library:
#include <pcl/io/pcd_io.h>
#include <pcl/filters/filter.h>
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