Multi-View Object Recognition Using View-Point Invariant Shape Relations and Appearance Information

We present an object recognition system coding shape by view-point invariant geometric relations and appearance. In our intelligent work-cell, the system can observe the work space of the robot by 3 pairs of Kinect and stereo cameras allowing for reliable and complete object information. We show that in such a set-up we can achieve high performance already with a low number of training samples. We show this by training the system to classify 56 objects using Random Forest algorithm. This indicates that our approach can be used in contexts such as assembly manipulation which require high reliability of object recognition.