Reduce the density of point cloud data

Below is a Python script for a ROS node that takes a point cloud and uses a Voxel Grid Filter to reduce the point cloud's density. You can use this as a starting point and customize it as needed for your specific ROS environment and point cloud topic.

#!/usr/bin/env python

import rospy
from sensor_msgs.msg import PointCloud2
from pcl import PointCloud
from pcl import VoxelGrid

def voxel_filter_callback(point_cloud_msg):
    # Convert PointCloud2 message to PCL PointCloud
    pcl_point_cloud = PointCloud()

    # Create a Voxel Grid filter object
    voxel_grid = VoxelGrid()
    # Set the voxel grid size (adjust as needed)
    voxel_grid.setLeafSize(0.01, 0.01, 0.01)

    # Filter the point cloud
    filtered_point_cloud = PointCloud()

    # Publish the filtered point cloud
    filtered_point_cloud_msg = filtered_point_cloud.to_msg()
    filtered_point_cloud_msg.header = point_cloud_msg.header

if __name__ == '__main__':
    # Point cloud topics for input and output (adjust as needed)
    input_point_cloud_topic = '/input_point_cloud'
    output_point_cloud_topic = '/output_point_cloud'

    # Create subscribers and publishers
    rospy.Subscriber(input_point_cloud_topic, PointCloud2, voxel_filter_callback)
    filtered_point_cloud_pub = rospy.Publisher(output_point_cloud_topic, PointCloud2, queue_size=10)


This node subscribes to an input point cloud topic (input_point_cloud), applies a Voxel Grid filter to it, and then publishes the filtered point cloud on an output topic (output_point_cloud). You can customize the voxel size by adjusting the setLeafSize parameters to meet your specific requirements.

Make sure you have the necessary dependencies, including the ROS environment and the PCL (Point Cloud Library) package, installed for this code to work correctly. Additionally, adjust the topics and parameters as needed for your ROS setup.