  As a consequence of an ever-increasing number of service robots, there is a
growing demand for highly accurate real-time 3D object recognition. Considering
the expansion of robot applications in more complex and dynamic environments,it
is evident that it is not possible to pre-program all object categories and
anticipate all exceptions in advance. Therefore, robots should have the
functionality to learn about new object categories in an open-ended fashion
while working in the environment.Towards this goal, we propose a deep transfer
learning approach to generate a scale- and pose-invariant object representation
by considering shape and texture information in multiple colorspaces. The
obtained global object representation is then fed to an instance-based object
category learning and recognition,where a non-expert human user exists in the
learning loop and can interactively guide the process of experience acquisition
by teaching new object categories, or by correcting insufficient or erroneous
categories. In this work, shape information encodes the common patterns of all
categories, while texture information is used to describes the appearance of
each instance in detail.Multiple color space combinations and network
architectures are evaluated to find the most descriptive system. Experimental
results showed that the proposed network architecture out-performed the
selected state-of-the-art approaches in terms of object classification accuracy
and scalability. Furthermore, we performed a real robot experiment in the
context of serve-a-beer scenario to show the real-time performance of the
proposed approach.
