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Initialization and system modeling in 3-D pose tracking

Initialization and system modeling in 3-D pose tracking

引用

18th International Conference on Pattern Recognition (ICPR 2006)

作者： Kragic, Danica Kyrki, Ville Computational Vision and Active Perception Centre Autonomous Systems KTH Sweden Laboratory of Information Processing Lappeenranta University of Technology Finland

ISBN: (纸本)0769525210

Initialization and choice of adequate motion models are two important but seldom discussed problems in 3D model-based pose (position and orientation) tracking. In this paper, we propose an automatic initialization approach suitable for textured objects. In addition, we define, study and experimentally evaluate three motion models commonly used in visual servoing and augmented reality.

关键词： Motion estimation

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Task modeling in imitation learning using latent variable models

Task modeling in imitation learning using latent variable mo...

引用

2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010

作者： Ek, Carl Henrik Song, Dan Huebner, Kai Kragic, Danica KTH - Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems Sweden

ISBN: (纸本)9781424486885

An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations. In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. Our model is capable to infer the task using only partial observations. Further, our framework allows the robot, given partial knowledge of the scene, to reason about what information streams to acquire in order to disambiguate the state-space the most. We present results for task classification within and also reason about different features discriminative power for different classes of tasks. ©2010 IEEE.

关键词： Intelligent robots

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Monocular real-time 3D articulated hand pose estimation

Monocular real-time 3D articulated hand pose estimation

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9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09

作者： Romero, Javier Kjellström, Hedvig Kragic, Danica Computational Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication SE-IOO 44 Stockholm Sweden

ISBN: (纸本)9781424445882

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 object 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 humanto-robot mapping. Experiment show the pose estimation to be more robust and accurate than a non-parametric method without temporal constraints. ©2009 IEEE.

关键词： Nearest neighbor search

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How behavior trees generalize the Teleo-Reactive paradigm and and-or-trees

How behavior trees generalize the Teleo-Reactive paradigm an...

引用

2016 IEEE/RSJ International Conference on Intelligent Robots and systems, IROS 2016

作者： Colledanchise, Michele Ögren, Petter Computer Vision and Active Perception Lab. Centre for Autonomous Systems School of Computer Science and Communication KTH - Royal Institute of Technology StockholmSE-100 44 Sweden

ISBN: (纸本)9781509037629

Behavior Trees (BTs) is a way of organizing the switching structure of a control system, that was originally developed in the computer gaming industry but is now also being used in robotics. The Teleo-Reactive programs (TRs) is a highly cited reactive hierarchical robot control approach suggested by Nilsson and And-Or-Trees are trees used for heuristic problems solving. In this paper, we show that BTs generalize TRs as well as And-Or-Trees, even though the two concepts are quite different. And-Or-Trees are trees of conditions, and we show that they transform into a feedback execution plan when written as a BT. TRs are hierarchical control structures, and we show how every TR can be written as a BT. Furthermore, we show that so-called Universal TRs, guaranteeing that the goal will be reached, are a special case of so-called Finite Time Successful BTs. This implies that many designs and theoretical results developed for TRs can be applied to BTs. © 2016 IEEE.

关键词： Forestry

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Object and pose recognition using contour and shape information

Object and pose recognition using contour and shape informat...

引用

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

作者： Cornelius, Hugo Kragic, Danica Eklundh, Jan-Olof Computational Vision and Active Perception Laboratory Centre for Autonomous Systems Stockholm Sweden Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)0780391772

Object recognition and pose estimation are of significant importance for robotic visual servoing, manipulation and grasping tasks. Traditionally, contour and shape based methods have been considered as most adequate for estimating stable and feasible grasps, [1]. More recently, a new research direction has been advocated in visual servoing where image moments are used to define a suitable error function to be minimized. Compared to appearance based methods, contour and shape based approaches are also suitable for use with range sensors such as, for example, lasers. In this paper, we evaluate a contour based object recognition system building on the method in [2], suitable for objects of uniform color properties such as cups, cutlery, fruits etc. This system is one of the building blocks of a more complex object recognition system based both on stereo and appearance cues, [3]. The system has a significant potential both in terms of service robot and programming by demonstration tasks. Experimental evaluation shows promising results in terms of robustness to occlusion and noise. © 2005 IEEE.

关键词： Industrial robots

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Grasping familiar objects using shape context

Grasping familiar objects using shape context

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International Conference on Advanced Robotics (ICAR)

作者： Jeannette Bohg Danica Kragic Centre of Autonomous Systems Computer Vision and Active Perception Laboratory Stockholm Sweden

We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for learning we use a supervised learning approach in which the classifier is trained with labeled synthetic images. Our results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects. Furthermore, we will show how our representation supports the inference of a full grasp configuration.

关键词： Humans Robot kinematics Robot vision systems Supervised learning Object detection Cameras Shape control computer vision Laboratories Prototypes

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Grasp recognition and mapping on humanoid robots

Grasp recognition and mapping on humanoid robots

引用

9th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS09

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

ISBN: (纸本)9781424445882

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. ©2009 IEEE.

关键词： Anthropomorphic robots

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Learning grasp stability based on tactile data and HMMs

Learning grasp stability based on tactile data and HMMs

引用

19th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2010

作者： Bekiroglu, Yasemin Kragic, Danica Kyrki, Ville Centre for Autonomous Systems and Computational Vision and Active Perception Lab. School of Computer Science and Communication KTH Stockholm Sweden Department of Information Technology Lappeenranta University of Technology Finland

ISBN: (纸本)9781424479917

In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system. © 2010 IEEE.

关键词： Hidden Markov models

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Learning task constraints for robot grasping using graphical models

Learning task constraints for robot grasping using graphical...

引用

23rd IEEE/RSJ 2010 International Conference on Intelligent Robots and systems, IROS 2010

作者： Song, D. Huebner, K. Kyrki, V. Kragic, D. KTH Royal Institute of Technology Stockholm Sweden Computer Vision and Active Perception Lab. Centre for Autonomous Systems Sweden Lappeenranta University of Technology Department of Information Technology Finland

ISBN: (纸本)9781424466757

This paper studies the learning of task constraints that allow grasp generation in a goal-directed manner. We show how an object representation and a grasp generated on it can be integrated with the task requirements. The scientific problems tackled are (i) identification and modeling of such task constraints, and (ii) integration between a semantically expressed goal of a task and quantitative constraint functions defined in the continuous object-action domains. We first define constraint functions given a set of object and action attributes, and then model the relationships between object, action, constraint features and the task using Bayesian networks. The probabilistic framework deals with uncertainty, combines apriori knowledge with observed data, and allows inference on target attributes given only partial observations. We present a system designed to structure data generation and constraint learning processes that is applicable to new tasks, embodiments and sensory data. The application of the task constraint model is demonstrated in a goal-directed imitation experiment. ©2010 IEEE.

关键词： Bayesian networks

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Fast and Automatic Detection and Segmentation of unknown objects

Fast and Automatic Detection and Segmentation of unknown obj...

引用

IEEE-RAS International Conference on Humanoid Robots

作者： Gert Kootstra Niklas Bergström Danica Kragic The Computer Vision and Active Perception Laboratory Center of Autonomous Systems Stockholm Sweden

This paper focuses on the fast and automatic detection and segmentation of unknown objects in unknown environments. Many existing object detection and segmentation methods assume prior knowledge about the object or human interference. However, an autonomous system operating in the real world will often be confronted with previously unseen objects. To solve this problem, we propose a segmentation approach named Automatic Detection And Segmentation (ADAS). For the detection of objects, we use symmetry, one of the Gestalt principles for figure-ground segregation to detect salient objects in a scene. From the initial seed, the object is segmented by iteratively applying graph cuts. We base the segmentation on both 2D and 3D cues: color, depth, and plane information. Instead of using a standard grid-based representation of the image, we use super pixels. Besides being a more natural representation, the use of super pixels greatly improves the processing time of the graph cuts, and provides more noise-robust color and depth information. The results show that both the object-detection as well as the object-segmentation method are successful and outperform existing methods.

关键词： Pixel Image segmentation Image color analysis Robots Databases Object detection Labeling

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