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检索条件"机构=Computer Vision and Active Perception Laboratory"

共 58 条记录，以下是21-30 订阅

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A probabilistic framework for task-oriented grasp stability assessment

A probabilistic framework for task-oriented grasp stability ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yasemin Bekiroglu Dan Song Lu Wang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.

关键词： Robot sensing systems Bayes methods Stability analysis Probabilistic logic Planning Grasping

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Cue integration through discriminative accumulation

Cue integration through discriminative accumulation

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Conference on computer vision and Pattern Recognition (CVPR)

作者： M.E. Nilsback B. Caputo Department of Numerical Analysis and Computer Science Computational Vision and Active Perception Laboratory (CVAP) KTH Stockholm Sweden

Object recognition systems aiming to work in real world settings should use multiple cues in order to achieve robustness. We present a new cue integration scheme, which extends the idea of cue accumulation to discriminative classifiers. We derive and test the scheme for support vector machines (SVMs), but we also show that it is easily extendible to any large margin classifier. In the case of one-class SVMs the scheme can be interpreted as a new class of Mercer kernels for multiple cues. Experimental comparison with a probabilistic accumulation scheme is favorable to our method. Comparison with voting scheme shows that our method may suffer as the number of object classes increases. Based on these results, we propose a recognition algorithm consisting of a decision tree where decisions at each node are taken using our accumulation scheme. Results obtained using this new algorithm compare very favorably to accumulation (both probabilistic and discriminative) and voting scheme.

关键词： Voting Object recognition Robustness computer vision Histograms Humans Laboratories Numerical analysis computer science Testing

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Learning a dictionary of prototypical grasp-predicting parts from grasping experience

Learning a dictionary of prototypical grasp-predicting parts...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Renaud Detry Carl Henrik Ek Marianna Madry Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. We focus our report on the definition of a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We present an experiment in which our agent extracts five prototypical parts from thirty-two real-world grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

关键词： Robots Shape Prototypes Three-dimensional displays Grasping Dictionaries Training

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Sparse summarization of robotic grasping data

Sparse summarization of robotic grasping data

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Martin Hjelm Carl Henrik Ek Renaud Detry Hedvig Kjellström Danica Kragic Centre for Autonomous Systems Computer Vision and Active Perception Laboratory CSC Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

We propose a new approach for learning a summarized representation of high dimensional continuous data. Our technique consists of a Bayesian non-parametric model capable of encoding high-dimensional data from complex distributions using a sparse summarization. Specifically, the method marries techniques from probabilistic dimensionality reduction and clustering. We apply the model to learn efficient representations of grasping data for two robotic scenarios.

关键词： Robots Data models Grasping Shape Kernel Optimization Encoding

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Integrating representative and discriminant models for object category detection

Integrating representative and discriminant models for objec...

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International Conference on computer vision (ICCV)

作者： M. Fritz B. Leibe B. Caputo B. Schiele Multimodal Interactive Systems Technical University of Darmstadt Germany Computer Vision and Active Perception Laboratory KTH Stockholm Sweden

Category detection is a lively area of research. While categorization algorithms tend to agree in using local descriptors, they differ in the choice of the classifier, with some using generative models and others discriminative approaches. This paper presents a method for object category detection which integrates a generative model with a discriminative classifier. For each object category, we generate an appearance codebook, which becomes a common vocabulary for the generative and discriminative methods. Given a query image, the generative part of the algorithm finds a set of hypotheses and estimates their support in location and scale. Then, the discriminative part verifies each hypothesis on the same codebook activations. The new algorithm exploits the strengths of both original methods, minimizing their weaknesses. Experiments on several databases show that our new approach performs better than its building blocks taken separately. Moreover, experiments on two challenging multi-scale databases show that our new algorithm outperforms previously reported results

关键词： Object detection Kernel Image segmentation Shape Voting Support vector machines Support vector machine classification computer vision Object recognition Visual databases

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Improving generalization for 3D object categorization with Global Structure Histograms

Improving generalization for 3D object categorization with G...

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2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Marianna Madry Carl Henrik Ek Renaud Detry Kaiyu Hang Danica Kragic Centre for Autonomous Systems and the Computer Vision and Active Perception Laboratory CSC KTH Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467317375

We propose a new object descriptor for three dimensional data named the Global Structure Histogram (GSH). The GSH encodes the structure of a local feature response on a coarse global scale, providing a beneficial trade-off between generalization and discrimination. Encoding the structural characteristics of an object allows us to retain low local variations while keeping the benefit of global representativeness. In an extensive experimental evaluation, we applied the framework to category-based object classification in realistic scenarios. We show results obtained by combining the GSH with several different local shape representations, and we demonstrate significant improvements to other state-of-the-art global descriptors.

关键词： Histograms Robustness Encoding Databases Image color analysis Shape Data models

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Visual recognition of grasps for human-to-robot mapping

Visual recognition of grasps for human-to-robot mapping

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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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Model-free robot manipulation of doors and drawers by means of fixed-grasps

Model-free robot manipulation of doors and drawers by means ...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yiannis Karayiannidis Christian Smith Francisco E. Viña Petter Ögren Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden

This paper addresses the problem of robot interaction with objects attached to the environment through joints such as doors or drawers. We propose a methodology that requires no prior knowledge of the objects' kinematics, including the type of joint - either prismatic or revolute. The method consists of a velocity controller which relies on force/torque measurements and estimation of the motion direction, rotational axis and the distance from the center of rotation. The method is suitable for any velocity controlled manipulator with a force/torque sensor at the end-effector. The force/torque control regulates the applied forces and torques within given constraints, while the velocity controller ensures that the end-effector moves with a task-related desired tangential velocity. The paper also provides a proof that the estimates converge to the actual values. The method is evaluated in different scenarios typically met in a household environment.

关键词： Torque Force Robots Hardware

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Grasping objects with holes: A topological approach

Grasping objects with holes: A topological approach

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Florian T. Pokorny Johannes A. Stork Danica Kragic Computer Vision and Active Perception Laboratory Centre for Autonomous Systems School of Computer Science and Communication Royal Institute of Technology Stockholm Sweden

ISBN: (纸本)9781467356404

This work proposes a topologically inspired approach for generating robot grasps on objects with `holes'. Starting from a noisy point-cloud, we generate a simplicial representation of an object of interest and use a recently developed method for approximating shortest homology generators to identify graspable loops. To control the movement of the robot hand, a topologically motivated coordinate system is used in order to wrap the hand around such loops. Finally, another concept from topology - namely the Gauss linking integral - is adapted to serve as evidence for secure caging grasps after a grasp has been executed. We evaluate our approach in simulation on a Barrett hand using several target objects of different sizes and shapes and present an initial experiment with real sensor data.

关键词： Noise measurement Robots Jacobian matrices

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Attentional Landmark Selection for Visual SLAM

Attentional Landmark Selection for Visual SLAM

引用

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Simone Frintrop Patric Jensfelt Henrik I. Christensen Computational Vision and Active Perception Laboratory (CVAP) Computer Science and Communication (CSC) Kungliga Tekniska Högskolan Stockholm Sweden

In this paper, we introduce a new method to automatically detect useful landmarks for visual SLAM. A biologically motivated attention system detects regions of interest which "pop-out" automatically due to strong contrasts and the uniqueness of features. This property makes the regions easily redetectable and thus they are useful candidates for visual landmarks. Matching based on scene prediction and feature similarity allows not only short-term tracking of the regions, but also redetection in loop closing situations. The paper demonstrates how regions are determined and how they are matched reliably. Various experimental results on real-world data show that the landmarks are useful with respect to be tracked in consecutive frames and to enable closing loops

关键词： Simultaneous localization and mapping Robot kinematics Tracking loops Mobile robots Layout Robot vision systems Cameras Focusing Intelligent robots Biology computing

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