  Despite the recent success of state-of-the-art 3D object recognition
approaches, service robots are frequently failed to recognize many objects in
real human-centric environments. For these robots, object recognition is a
challenging task due to the high demand for accurate and real-time response
under changing and unpredictable environmental conditions. Most of the recent
approaches use either the shape information only and ignore the role of color
information or vice versa. Furthermore, they mainly utilize the $L_n$ Minkowski
family functions to measure the similarity of two object views, while there are
various distance measures that are applicable to compare two object views. In
this paper, we explore the importance of shape information, color constancy,
color spaces, and various similarity measures in open-ended 3D object
recognition. Towards this goal, we extensively evaluate the performance of
object recognition approaches in three different configurations, including
\textit{color-only}, \textit{shape-only}, and \textit{ combinations of color
and shape}, in both offline and online settings. Experimental results
concerning scalability, memory usage, and object recognition performance show
that all of the \textit{combinations of color and shape} yields significant
improvements over the \textit{shape-only} and \textit{color-only} approaches.
The underlying reason is that color information is an important feature to
distinguish objects that have very similar geometric properties with different
colors and vice versa. Moreover, by combining color and shape information, we
demonstrate that the robot can learn new object categories from very few
training examples in a real-world setting.
