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Estimation du prol de la chaussee pour la dynamique du vehicule
221-226
4emes Journees Doctorales/Journees Nationales MACS 2011 (JD-JN-MACS 2011)
2011
Calibração de Imagens Estereoscópicas de Objetos em 3 dimensões para Geração Automática de Trajetórias de Manipuladores Robóticos Industriais
X Simpósio Brasileiro de Automação Inteligente (SBAI)
2011
Approximation of probability density functions on the Euclidean group parametrized by dual quaternions
Perception is fundamental to many robot application areas especially in service robotics. Our aim is to perceive and model an unprepared kitchen scenario with many objects. We start with the perception of a single target object. The modeling relies especially on fusing and merging of weak information from the sensors of the robot in order to localize objects. This requires the representation of various probability distributions of pose in S3xR3 as orientation and position have to be localized. In this thesis I present a framework for probabilistic modeling of poses in S3xR3 that represents a large class of probability distributions and provides among others the operations of the fusion and the merge of estimates. Further it offers the propagation of uncertain information data. I work out why we choose to represent the orientation part of a pose by a unit quaternion. The translation part is described either by a 3-dimensional vector or by a purely imaginary quaternion. This depends on whether we denote the probability density function or whether we want to represent a transformation which consists of a rotation and a translation by a dual quaternion. A basic probability density function over the poses is defined by a tangent point on the hypersphere and a 6-dimensional Gaussian distribution. The hypersphere is embedded to the R4 which is representing a unit quaternions whereas the Gaussian is defined over the product of the tangent space of the sphere and of the space of translations. The projection of this Gaussian to the hypersphere induces a distribution over poses in S3xR3. The set of mixtures of projected Gaussians can approximate the probability density functions that arise in our application. Moreover it is closed under the operations introduced in this framework and allows for an efficient implementation.
2011
Cognitive Control
2011
Sliding Mode Leader Following Control for Autonomous Air Robots
In Proceedings of the IEEE/SICE International Symposium on System Integration (SII)
2011
Haptic and Visual Influences on Grasp Point Selection
Journal of Motor Behavior
2011
43
6
427-431
Light touch for balance: influence of a time-varying external driving signal
Philosophical Transactions of the Royal Society B: Biological Sciences
2011
366
1581
3133-3141
MPG - A Framework for Reasoning on 6 DOF Pose Uncertainty
2011
Towards an Objective Quality Evaluation Framework for Haptic Data Reduction
IEEE World Haptics Conference
2011
Modeling joint action as human movement synchronization in goal-directed tasks
2011