Revision as of 09:21, 25 February 2014

Go to root: PhD-3D-Object-Tracking

It is time to learn the basics of one of the most interesting applications of point cloud processing: 3D object recognition. Akin to 2D recognition, this technique relies on finding good keypoints (characteristic points) in the cloud, and matching them to a set of previously saved ones. But 3D has several advantages over 2D: namely, we will be able to estimate with decent accuraccy the exact position and orientation of the object, relative to the sensor. Also, 3D object recognition tends to be more robust to clutter (crowded scenes where objects in the front occluding objects in the background). And finally, having information about the object's shape will help with collision avoidance or grasping operations.

In this first tutorial we will see what descriptors are, how many types are there available in PCL, and how to compute them.

Overview

The basis of 3D object recognition is to find a set of correspondences between two different clouds, one of them containing the object we are looking for. In order to do this, we need a way of

Go to root: PhD-3D-Object-Tracking

Links to articles:

PCL/OpenNI tutorial 0: The very basics

PCL/OpenNI tutorial 1: Installing and testing

PCL/OpenNI tutorial 2: Cloud processing (basic)

PCL/OpenNI tutorial 3: Cloud processing (advanced)

PCL/OpenNI tutorial 4: 3D object recognition (descriptors)

PCL/OpenNI troubleshooting