Linear periodic control: A frequency domain viewpoint
Systems & Control Letters
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Presence: Teleoperators and Virtual Environments
Pose and motion estimation from vision using dual quaternion-based extended kalman filtering
Pose and motion estimation from vision using dual quaternion-based extended kalman filtering
Fusion of Vision and Inertial Data for Motion and Structure Estimation
Journal of Robotic Systems
Global Positioning Systems, Inertial Navigation, and Integration
Global Positioning Systems, Inertial Navigation, and Integration
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Simultaneous Motion and Structure Estimation by Fusion of Inertial and Vision Data
International Journal of Robotics Research
On multi-rate fusion for non-linear sampled-data systems: Application to a 6D tracking system
Robotics and Autonomous Systems
Inertially Aided Visual Odometry for Miniature Air Vehicles in GPS-denied Environments
Journal of Intelligent and Robotic Systems
A nonlinear observer for 6 DOF pose estimation from inertial and bearing measurements
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Augmented reality for art, design and cultural heritage: system design and evaluation
Journal on Image and Video Processing - Special issue on image and video processing for cultural heritage
On accurate localization and uncertain sensors
International Journal of Intelligent Systems
A wearable visuo-inertial interface for humanoid robot control
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Closed-Form Solution of Visual-Inertial Structure from Motion
International Journal of Computer Vision
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This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower. the proposed approach uses a constant linear acceleration model and constant angular velocity model based on quaternions, which yields a non-linear model for states and a linear model in measurement equations. Results show that a significant improvement is obtained on the estimation when fusing both measurements with respect to just vision or just inertial measurements. It is also shown that the proposed system can estimate fast-motions even when vision system fails. Moreover, a study of the influence of the noise covariance is also performed, which aims to select their appropriate values at the tuning process. The setup is an end-effector mounted camera, which allow us to pre-define basic rotational and translational motions for validating results.