output
20112026
most citedQuaternion kinematics for the error-state Kalman filter

410 citations

Showing 2024Show all

6 papers · 1 filter

cs.CV2024★ 1 cited

2by2: Weakly-Supervised Learning for Global Action Segmentation

Elena Bueno-Benito, Mariella Dimiccoli

This paper presents a simple yet effective approach for the poorly investigated task of global action segmentation, aiming at grouping frames capturing the same action across video…

cs.RO2024★ 5 cited

Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization

Oscar Gil Viyuela, Alberto Sanfeliu

Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among…

cs.CV2024

MEGA: Masked Generative Autoencoder for Human Mesh Recovery

Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda +1

Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless,…

cs.CV2024★ 2 cited

PnLCalib: Sports Field Registration via Points and Lines Optimization

Marc Gutiérrez-Pérez, Antonio Agudo

Camera calibration in broadcast sports videos presents numerous challenges for accurate sports field registration due to multiple camera angles, varying camera parameters, and freq…

math.OC2024★ 4 cited

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method

Edoardo Caldarelli, Antoine Chatalic, Adrià Colomé +4

In this paper, we study how the Koopman operator framework can be combined with kernel methods to effectively control nonlinear dynamical systems. While kernel methods have typical…

cs.RO2024★ 7 cited

Morphological Symmetries in Robotics

Daniel Ordoñez-Apraez, Giulio Turrisi, Vladimir Kostic +6

We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot's morphology, frequently o…