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 .
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 .
D
camera-based
R
impossible
not possible
R
possible object categories . Robots will have to be able to learn
object categories and anticipate all exceptions in advance. Therefore, robots should have the functionality to learn about
R
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
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
R
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
Experimental results showed that the proposed network architecture out-performed the selected
R
algorithms
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