These exercises are part of the perception lessons in the Udacity Robotics Nanodegree Program In these exercises, you will perform object segmentation on 3D point cloud data using python-pcl to leverage the power of the Point Cloud Library. 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!
Objects segmented using clustering
Objects segmented using RANSAC Plane Segmentation
Table segmented using RANSAC Plane Segmentation
$ sudo pip install cython
$ cd ~/RoboND-Perception-Exercises/python-pcl
$ python setup.py build
$ sudo python setup.py install
$ sudo apt-get install pcl-tools
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:
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