Semantic Understanding for Robot Perception
Advancements in robotic navigation and fetch and delivery tasks rest to a large extent on robust, efficient and scalable semantic understanding of the surrounding environment. Deep learning fueled rapid progress in computer vision in object category recognition, localization and semantic segmentation, exploiting large amounts of labelled data and using mostly static images. I will talk about challenges and opportunities in tackling these problems in indoors and outdoors environments relevant to robotics applications. These include methods for semantic segmentation and 3D structure recovery using deep convolutional neural networks (CNNs), localization and mapping of large scale environments, training object instance detectors using synthetically generated training data and 3D object pose recovery. The applicability of the techniques for autonomous driving, service robotics, manipulation and navigation will be discussed.
Jana Kosecka is a Professor at the Department of Computer Science, George Mason University and currently a Visiting Research scientist at Google. She is the recipient of David Marr's prize in Computer Vision and received the National Science Foundation CAREER Award. Jana is an Associate Editor of IEEE Robotics and Automation Letters, Member of the Editorial Board of International Journal of Computer Vision and Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. She has numerous publications in refereed journals and conferences and is a co-author of a monograph titled Invitation to 3D vision: From Images to Geometric Models. Her general research interests are in Computer Vision and Robotics. In particular she is interested 'seeing' systems engaged in autonomous tasks, acquisition of static and dynamic models of environments by means of visual sensing, object recognition and human-computer interaction.