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Monocular real-time 3D articulated hand pose estimation

Monocular real-time 3D articulated hand pose estimation

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Javier Romero Hedvig Kjellström Danica Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424445875

Markerless, vision based estimation of human hand pose over time is a prerequisite for a number of robotics applications, such as learning by demonstration (LbD), health monitoring, teleoperation, human-robot interaction. It has special interest in humanoid platforms, where the number of degrees of freedom makes conventional programming challenging. Our primary application is LbD in natural environments where the humanoid robot learns how to grasp and manipulate objects by observing a human performing a task. This paper presents a method for continuous vision based estimation of human hand pose. The method is non-parametric, performing a nearest neighbor search in a large database (100000 entries) of hand pose examples. The main contribution is a real time system, robust to partial occlusions and segmentation errors, that provides full hand pose recognition from markerless data. An additional contribution is the modeling of constraints based on temporal consistency in hand pose, without explicitly tracking the hand in the high dimensional pose space. The pose representation is rich enough to enable a descriptive human-to-robot mapping. Experiments show the pose estimation to be more robust and accurate than a non-parametric method without temporal constraints.

关键词： Humanoid robots Real time systems Robustness Human robot interaction Robot vision systems Fingers computer vision computer science Application software computerized monitoring

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Grasp recognition and mapping on humanoid robots

Grasp recognition and mapping on humanoid robots

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Martin Do Javier Romero Hedvig Kjellström Pedram Azad Tamim Asfour Danica Kragic Rüdiger Dillmann Institute for Anthropomatics University of Karlsruhe Karlsruhe Germany Royal Institute of Technology Computer Vision & Active Perception Laboratory Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781424445875

In this paper, we present a system for vision-based grasp recognition, mapping and execution on a humanoid robot to provide an intuitive and natural communication channel between humans and humanoids. This channel enables a human user to teach a robot how to grasp an object. The system comprises three components: human upper body motion capture system which provides the approaching direction towards an object, hand pose estimation and grasp recognition system, which provides the grasp type performed by the human as well as a grasp mapping and execution system for grasp reproduction on a humanoid robot with five-fingered hands. All three components are real-time and markerless. Once an object is reached, the hand posture is estimated, including hand orientation and grasp type. For the execution on a robot, hand posture and approach movement are mapped and optimized according to the kinematic limitations of the robot. Experimental results are performed on the humanoid robot ARMAR-IIIb.

关键词： Humanoid robots Humans Educational robots Robot vision systems Communication channels Motion estimation Kinematics Robot programming Education Anthropomorphism

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Visual recognition of grasps for human-to-robot mapping

Visual recognition of grasps for human-to-robot mapping

引用

2008 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Hedvig Kjellstrom Javier Romero Danica Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication KTH Stockholm Sweden

ISBN: (纸本)9781424420575

This paper presents a vision based method for grasp classification. It is developed as part of a Programming by Demonstration (PbD) system for which recognition of objects and pick-and-place actions represent basic building blocks for task learning. In contrary to earlier approaches, no articulated 3D reconstruction of the hand over time is taking place. The indata consists of a single image of the human hand. A 2D representation of the hand shape, based on gradient orientation histograms, is extracted from the image. The hand shape is then classified as one of six grasps by finding similar hand shapes in a large database of grasp images. The database search is performed using Locality Sensitive Hashing (LSH), an approximate k-nearest neighbor approach. The nearest neighbors also give an estimated hand orientation with respect to the camera. The six human grasps are mapped to three Barret hand grasps. Depending on the type of robot grasp, a precomputed grasp strategy is selected. The strategy is further parameterized by the orientation of the hand relative to the object. To evaluate the potential for the method to be part of a robust vision system, experiments were performed, comparing classification results to a baseline of human classification performance. The experiments showed the LSH recognition performance to be comparable to human performance.

关键词： Robots Three dimensional displays Image segmentation Image reconstruction Humans Robot sensing systems Distance measurement

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Modeling and recognition of actions through motor primitives

Modeling and recognition of actions through motor primitives

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： David Martinez Danica Kragic Computational Vision and Active Perception Lab Centre for Autonomous Systems KTH Royal Institute of Technology Stockholm Sweden

We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination of discriminative (support vector machines, conditional random fields) and generative approaches (hidden Markov models). We examine the hypothesis that complex actions can be represented as a sequence of motion or action primitives. The experimental evaluation, performed with five object manipulation actions and 10 people, investigates the modeling approach of the primitive action structure and compares the performance of the considered generative and discriminative models.

关键词： Hidden Markov models Support vector machines Humans Support vector machine classification Robotics and automation Magnetic sensors Biological system modeling computer vision Robot sensing systems USA Councils

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Early reactive grasping with second order 3D feature relations

引用

13th International Conference on Advanced Robotics, ICAR 2007

作者： Aarno, Daniel Sommerfeld, Johan Kragic, Danica Pugeault, Nicolas Kalkan, Sinan Wörgötter, Florentin Kraft, Dirk Krüger, Norbert Centre for Autonomous Systems Computational Vision and Active Perception School of Computer Science and Communication Royal Institute of Technology Stockholm10044 Sweden School of Informatics University of Edinburgh 2 Buccleuch Place EdinburghEH8 9LW United Kingdom Bernstein Center for Computational Neuroscience University of Goettingen Bunsenstr. 10 Goettingen37073 Germany The Maersk Mc-Kinney Moller Institute University of Southern Denmark Campusvej 55 Odense M5230 Denmark

ISBN: (纸本)9783540767282

One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development. The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment. © Springer-Verlag Berlin Heidelberg 2008.

关键词： Cognitive systems

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Layered HMM for Motion Intention Recognition

Layered HMM for Motion Intention Recognition

引用

2006 IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Daniel Aarno Danica Kragic School of Computer Science and Communication Centre for Autonomous Systems-Computational Vision and Active Perception Laboratory Royal Institute of Technology Sweden

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers

关键词： Hidden Markov models Intelligent robots Man machine systems Collaboration Learning Human robot interaction Robotics and automation Speech recognition Character recognition computer vision

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Constrained path planning and task-consistent path adaptation for mobile manipulators

Constrained path planning and task-consistent path adaptatio...

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12th International Conference on Advanced Robotics, 2005. ICAR '05

作者： Aarno, Daniel Lingelbach, Frank Kragić, Danica Centre for Autonomous Systems Computational Vision and Active Perception Lab. KTH Stockholm Sweden Centre for Autonomous Systems S3 - Automatic Control KTH Stockholm Sweden

ISBN: (纸本)0780391772

This paper presents our ongoing research in the design of a versatile service robot capable of operating in a home or office environment. Ideas presented here cover architectural issues and possible applications for such a robot system with focus on tasks requiring constrained end-effector motions. Two key components of such system is a path planner and a reactive behavior capable of force relaxation and path adaptation. These components are presented in detail along with an overview of the software architecture they fit into. © 2005 IEEE.

关键词： Motion planning

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Integrating object and grasp recognition for dynamic scene interpretation

Integrating object and grasp recognition for dynamic scene i...

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International Conference on Advanced Robotics (ICAR)

作者： S. Ekvall D. Kragic Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Department of Numerical Analysis and Computer Science Royal Institute of Technology Stockholm Sweden

Understanding and interpreting dynamic scenes and activities is a very challenging problem. In this paper, we present a system capable of learning robot tasks from demonstration. Classical robot task programming requires an experienced programmer and a lot of tedious work. In contrast, programming by demonstration is a flexible framework that reduces the complexity of programming robot tasks, and allows end-users to demonstrate the tasks instead of writing code. We present our recent steps towards this goal. A system for learning pick-and-place tasks by manually demonstrating them is presented. Each demonstrated task is described by an abstract model involving a set of simple tasks such as what object is moved, where it is moved, and which grasp type was used to move it

关键词： Layout Robot programming Writing Humans Sensor phenomena and characterization Object recognition computer vision laboratories Numerical analysis computer science

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Combination of foveal and peripheral vision for object recognition and pose estimation

Combination of foveal and peripheral vision for object recog...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： M. Bjorkman D. Kragic Centre for Autonomous Systems & Computer Vision and Active Perception Laboratory Royal Institute of Technology Stockholm Sweden

In this paper, we present a real-time vision system that integrates a number of algorithms using monocular and binocular cues to achieve robustness in realistic settings, for tasks such as object recognition, tracking and pose estimation. The system consists of two sets of binocular cameras; a peripheral set for disparity based attention and a foveal one for higher level processes. Thus the conflicting requirements of a wide field of view and high resolution can be overcome. One important property of the system is that the step from task specification through object recognition to pose estimation is completely automatic, combining both appearance and geometric models. Experimental evaluation is performed in a realistic indoor environment with occlusions, clutter, changing lighting and background conditions.

关键词： Object recognition Cameras Machine vision Robustness Layout Image segmentation Image recognition computer vision Real time systems Solid modeling

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Tracking techniques for visual servoing tasks

Tracking techniques for visual servoing tasks

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： D. Kragic H.I. Christensen Computer Vision and Active Perception Lab Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden Centre for Autonomous Sys Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

Many of today's visual servoing systems rely on the use of markers on the object to provide features for control. There is thus a need for a visual system that provides control features regardless of the appearance of the object. Region based tracking is a natural approach since it does not require any special type of features. In this paper we present two different approaches to region based tracking: 1) a multi-resolution gradient based approach (using optical flow); and 2) a discrete feature based search approach. We present experiments conducted with both techniques for different types of image motions. Finally, the performance, drawbacks and limitations of used techniques are discussed.

关键词： Visual servoing Robots Tracking computer vision Numerical analysis Control systems Grippers Image motion analysis Motion estimation Visual system

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