3D object recognition for anthropomorphic robots performing tracking tasks

Object recognition is still a major research issue of particular relevance in robotics. The new trend in industrial and mainly in service robotics is to perform manipulation tasks in an unstructured environment working in synergy with humans. To perform tasks in an environment that is not perfectly controlled, robots need adequate perceptual capabilities. Among various types of sensors available for robotic systems, the time-of-flight (ToF) camera is one of the most utilized since it simultaneously provides intensity and depth data at a high frame rate. Our proposal makes use of this technology exploiting both, depth and grey-scale information. Therefore, intensity and geometric features are fused together to allow 3D object recognition in real scenes in presence of partial occlusions. As a case study, an object tracking task for an anthropomorphic robot is presented. Experimental results demonstrate the effectiveness of the proposed method, not only providing reliable visual information about the object to be tracked but also recognizing potential obstacles which should be avoided during the robot arm movement.