Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Superior augmented reality registration by integrating landmark tracking and magnetic tracking
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Recent Advances in Augmented Reality
IEEE Computer Graphics and Applications
Presence: Teleoperators and Virtual Environments
Hybrid Inertial and Vision Tracking for Augmented Reality Registration
VR '99 Proceedings of the IEEE Virtual Reality
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Building a Hybrid Tracking System: Integration of Optical and Magnetic Tracking
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
On the hybrid aid-localization for outdoor augmented reality applications
Proceedings of the 2008 ACM symposium on Virtual reality software and technology
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A computational model for the integration of linked data in mobile augmented reality applications
Proceedings of the 8th International Conference on Semantic Systems
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In this paper we present an efficient algorithm for estimating the 3D localization in an urban environments by fusing measurements from GPS receiver, inertial sensor and vision. Such hybrid sensor is important for numerous applications including outdoor mobile augmented reality and 3D robot localization. Our approach is based on non-linear filtering of these complementary sensors using a multi-rate Extended Kalman Filter. Our main contributions concern the modeling of the sensor fusion and the development of an efficient approach for camera pose tracking using only natural features. This method improves the accuracy of the estimated 3D localization. We evaluated the performances of our approach and demonstrated its effectiveness through experiments on real data.