Iterative pose estimation using coplanar feature points
Computer Vision and Image Understanding
Three D-Dynamic Scene Analysis: A Stereo Based Approach
Three D-Dynamic Scene Analysis: A Stereo Based Approach
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
VIS-Tracker: A Wearable Vision-Inertial Self-Tracker
VR '03 Proceedings of the IEEE Virtual Reality 2003
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Robust Hybrid Tracking System for Outdoor Augmented Reality
VR '04 Proceedings of the IEEE Virtual Reality 2004
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Real-time implementation of airborne inertial-SLAM
Robotics and Autonomous Systems
Development of a Tiny Orientation Estimation Device to Operate under Motion and Magnetic Disturbance
International Journal of Robotics Research
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Online camera pose estimation in partially known and dynamic scenes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
Going out: robust model-based tracking for outdoor augmented reality
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Complexity analysis of the marginalized particle filter
IEEE Transactions on Signal Processing
Marginalized particle filters for mixed linear/nonlinear state-space models
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
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The use of a particle filter (PF) for camera pose estimation is an ongoing topic in the robotics and computer vision community, especially since the FastSLAM algorithm has been utilised for simultaneous localisation and mapping (SLAM) applications with a single camera. The major problem in this context consists in the poor proposal distribution of the camera pose particles obtained from the weak motion model of a camera moved freely in 3D space. While the FastSLAM 2.0 extension is one possibility to improve the proposal distribution, this paper addresses the question of how to use measurements from low-cost inertial sensors (gyroscopes and accelerometers) to compensate for the missing control information. However, the integration of inertial data requires the additional estimation of sensor biases, velocities and potentially accelerations, resulting in a state dimension, which is not manageable by a standard PF. Therefore, the contribution of this paper consists in developing a real-time capable sensor fusion strategy based upon the marginalised particle filter (MPF) framework. The performance of the proposed strategy is evaluated in combination with a marker-based tracking system and results from a comparison with previous visual-inertial fusion strategies based upon the extended Kalman filter (EKF), the standard PF and the MPF are presented.