Assistance via IoT Networking cameras and Evidence Theory for 3D Object Instance Recognition:Application for the NAO Humanoid Robot

Abstract Object recognition problem based on cameras has become one of the most important tasks in robotics. Nevertheless, for a robot equipped with a camera and evolving in uncontrolled environment, many factors can limit the quality of its recognition, such as lighting conditions, conflict situations, occlusion, and cameras orientations, etc. Under theses imperfections, a single robot camera can provide incomplete and unreliable information and is thus limited to deal with complex recognition situations, particularly in imprecise and uncertain environments. Under such circumstances, in this paper, an IoT (Internet of Things) multi-camera embedded 3D object recognition system, including a NAO humanoid robot and other IoT heterogeneous cameras (2D and 3D), is proposed. These cameras can be complementary, and their combination increases the recognition reliability of the single robot camera. The used IoT infrastructure allows the communication between the robot and different connected cameras using internet protocols. The proposed recognition system uses evidence theory to fuse the data issued from these IoT sources for making the recognition decision. This paper aims at demonstrating the advantages of coupling the IoT technology with the evidence theory concepts for improving the 3D object recognition for the NAO robot. Through the proposed application, our method has been validated and its performance has been compared with a deep learning approach.