ROS Adventure

q removing the outlier features q extract the region of interest by computing a bounding-box rectangle around the inlier features.

If you are interested in obtaining more informations about in hand object modeling please have a look over the next tutorial. You can find the source code and also a briefly description about how to run the program . http://www.ros.org/wiki/model_completion

Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinect style) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. Recently, sensors combining RGB images with depth measurements (RGB-D sensors) have come to prominence due to their gaming applications and in particular due to the release of the Xbox 360 Kinect (Microsoft, 2010). Such sensors are now both very affordable (around $150) and readily available, making them ideal for personal robotics applications.

Recognizing and manipulating objects is an important task for mobile robots  performing useful services in everyday environments. While existing techniques  for object recognition related to manipulation provide very good results even for  noisy and incomplete data, they are typically trained using data generated in an offline  process.Specifically, we develop an approach to building a 3D surface model of an unknown object based on data collected by a kinect sensor observing the robot’s hand moving the object.