Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Introduction to Algorithms
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
ARTag, a Fiducial Marker System Using Digital Techniques
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Preemptive RANSAC for live structure and motion estimation
Machine Vision and Applications
Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Monocular model-based 3D tracking of rigid objects
Foundations and Trends® in Computer Graphics and Vision
Skeletal parameter estimation from optical motion capture data
SIGGRAPH '04 ACM SIGGRAPH 2004 Sketches
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
The Journal of Machine Learning Research
Visual door detection integrating appearance and shape cues
Robotics and Autonomous Systems
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Towards 3D Point cloud based object maps for household environments
Robotics and Autonomous Systems
Unsupervised Learning of Skeletons from Motion
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Probabilistic mobile manipulation in dynamic environments, with application to opening doors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning Bayesian network equivalence classes with Ant Colony optimization
Journal of Artificial Intelligence Research
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
Nonlinear constraint network optimization for efficient map learning
IEEE Transactions on Intelligent Transportation Systems
Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
HERB: a home exploring robotic butler
Autonomous Robots
High-accuracy 3D sensing for mobile manipulation: improving object detection and door opening
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Combined Region and Motion-Based 3D Tracking of Rigid and Articulated Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kinematic-parameter identification for serial-robot calibration based on POE formula
IEEE Transactions on Robotics
Compliant control of multicontact and center-of-mass behaviors in humanoid robots
IEEE Transactions on Robotics
Hierarchical object discovery and dense modelling from motion cues in RGB-D video
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Learning probabilistic models for mobile manipulation robots
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic relationship. In particular, we present a set of parametric and non-parametric edge models and how they can robustly be estimated from noisy pose observations. We furthermore describe how to estimate the kinematic structure and how to use the learned kinematic models for pose prediction and for robotic manipulation tasks. We finally present how the learned models can be generalized to new and previously unseen objects. In various experiments using real robots with different camera systems as well as in simulation, we show that our approach is valid, accurate and efficient. Further, we demonstrate that our approach has a broad set of applications, in particular for the emerging fields of mobile manipulation and service robotics.