3D Point Cloud Based Object Recognition System

crossposted from willowgarage.com

Bastian Steder, a PhD student from the Autonomous Intelligent Systems Group at the University of Freiburg, Germany, spent the summer at Willow Garage implementing an object recognition system using 3D point cloud data. With 3D sensors becoming cheaper and more widely available, they are a valuable tool for robot perception. 3D data provides extra information to a robot, such as distance and shape, that enables different approaches to identifying objects in the world. Bastian's work focused on using databases of 3D models to identify objects in this 3D sensor data.

The main focus for Bastian's work was on the feature-extraction process for 3D data. One of his contributions was a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds. This method explicitly considers the borders of the objects identified by transitions from foreground to background. Bastian also developed a new feature descriptor type, called NARF (Normal Aligned Radial Features), that takes the same information into account. Based on these feature matches, Bastian then worked on a process to create a set of potential object poses and added spatial verification steps to assure these observations fit the sensor data.

The full system can identify the existence and poses of arbitrary objects, of which we have a point cloud model in a very efficient manner, using only the geometrical information provided by the 3D sensor. Code for Bastian's work, including object recognition and feature extraction, has been integrated with PCL, which is a general library for 3D geometry processing in development at Willow Garage. To find out more, check out the point_cloud_perception stack on ROS.org. For detailed technical information, you can check out Bastian's presentation slides below (download PDF).