Robust single-view instance recognition

Abstract

Some robots must repeatedly interact with a fixed set of objects in their environment. To operate correctly, it is helpful for the robot to be able to recognize the object instances that it repeatedly encounters. However, current methods for recognizing object instances require that, during training, many pictures are taken of each object from a large number of viewing angles. This procedure is slow and requires much manual effort before the robot can begin to operate in a new environment. We have developed a novel procedure for training a neural network to recognize a set of objects from just a single training image per object. To obtain robustness to changes in viewpoint, we take advantage of a supplementary dataset in which we observe a separate (non-overlapping) set of objects from multiple viewpoints. After pre-training the network in a novel multi-stage fashion, the network can robustly recognize new object instances given just a single training image of each object. If more images of each object are available, the performance improves. We perform a thorough analysis comparing our novel training procedure to traditional neural network pre-training techniques as well as previous state-of-the-art approaches including keypoint-matching, template-matching, and sparse coding, and we demonstrate that our method significantly outperforms these previous approaches. Our method can thus be used to easily teach a robot to recognize a novel set of object instances from unknown viewpoints.