Combining Shape Features with Multiple Color Spaces in Open-Ended 3D Object Recognition

As a consequence of an ever-increasing number of camera-based 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 impossible to pre-program all possible object categories. Robots will have to be able to learn new object categories in the field. The network architecture proposed in this work expands from the OrthographicNet, an approach recently proposed by Kasaei et al., using a deep transfer learning strategy which not only meets the aforementioned requirements but additionally generates a scale and rotation-invariant reference frame for the classification of objects. In its current iteration, the OrthographicNet only uses shape-information. With the addition of multiple color spaces, the upgraded network architecture proposed here, can achieve an even higher descriptiveness while simultaneously increasing the robustness of predictions for similarly shaped objects. Multiple color space combinations and network architectures are evaluated to find the most descriptive system. However, this performance increase is not achieved at the cost of longer processing times, because any system deployed in robotic applications will need the ability to provide real-time information about its environment. Experimental results show that the proposed network architecture ranks competitively among other state-of-the-art algorithms.