J
Joris De Schutter
Researcher at Core Laboratories
Publications - 275
Citations - 5138
Joris De Schutter is an academic researcher from Core Laboratories. The author has contributed to research in topics: Robot & Task (project management). The author has an hindex of 32, co-authored 275 publications receiving 4524 citations. Previous affiliations of Joris De Schutter include De La Salle University & Katholieke Universiteit Leuven.
Papers
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An integrated friction model structure with improved presliding behaviour for accurate friction compensation
TL;DR: The general friction model allows modeling of individual friction systems through the identification of a set of parameters that determine the complete behavior of the system and has been used to identify the friction behavior of a linear slide as well as that of the KUKA robot.
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Kalman filters for non-linear systems: a comparison of performance
TL;DR: In this article, the authors provide an application-independent analysis of the performance of the common Kalman filter variants in a non-linear system with uncorrelated uncertainties, which is the original formulation of the KF.
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Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty
Joris De Schutter,Tinne De Laet,J. Rutgeerts,Wilm Decré,Ruben Smits,Erwin Aertbeliën,Kasper Claes,Herman Bruyninckx +7 more
TL;DR: This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints, which integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework.
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Brief paper: Robust high-order repetitive control: Optimal performance trade-offs
TL;DR: This paper presents a systematic, semidefinite programming based approach to compute high-order repetitive controllers that yield an optimal trade-off between these two performance criteria.
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An adaptable system for RGB-D based human body detection and pose estimation
Koen Buys,Cedric Cagniart,Anatoly Baksheev,Tinne De Laet,Joris De Schutter,Caroline Pantofaru +5 more
TL;DR: A customizable human kinematic model that extracts skeletons from RGB-D sensor data that adapts on-line to difficult unstructured scenes taken from a moving camera and benefits from using both color and depth data is presented.