In Exercise 1, you'll get some practice performing filtering and RANSAC plane segmentation, and in Exercise-2 you'll write a ROS node to perform those functions as well as Euclidean Clustering for object segmentation! Once you have cloned or downloaded this repo in the provided VM, you can follow the steps given below for installation.

Installation

pcl is naturely installed with ubuntu

however, we need to install a python 3.8 binding with pcl library

sudo apt-get update
sudo apt-get install python3-pcl pcl-tools

Documentation for pcl_helper.py

pcl_helper.py contains useful functions for working with point cloud data with ROS and PCL. The file itself is located in Exercise-2/sensor_stick/scripts/. While the helper functions are required for Exercise-2, they could also come in handy if you want to explore more deeply in Exercise-1. Here's a brief description of the contents:

Functions:

random_color_gen()

Generates a random set of r,g,b values
Return: a 3-tuple with r,g,b values (range 0-255)

ros_to_pcl(sensor_msgs/PointCloud2)

Converts sensor_msgs/PointCloud2 to XYZRGB Point Cloud
Return: pcl.PointCloud_PointXYZRGB

pcl_to_ros(pcl.PointCloud_PointXYZRGB)

Converts XYZRGB Point Cloud to sensor_msgs/PointCloud2
Return: sensor_msgs/PointCloud2

XYZRGB_to_XYZ(XYZRGB_cloud)

Converts XYZRGB Point Cloud to XYZ Point CLoud
Return: pcl.PointCloud

XYZ_to_XYZRGB(XYZ_cloud, color)

Takes a 3-tuple as color and adds it to XYZ Point Cloud
Return: pcl.PointCloud_PointXYZRGB

rgb_to_float(color)

Converts 3-tuple color to a single float32
Return: rgb packed as a single float32

get_color_list(cluster_count)

Creates a list of 3-tuple (rgb) with length of the list = cluster_count
Return: get_color_list.color_list