Camera Pose Estimation by Vision-inertial Sensor Fusion: An Application to Augmented Reality Books

2016 ◽  
Vol 2016 (4) ◽  
pp. 1-6
Author(s):  
Juan Li ◽  
Hamid Aghajan ◽  
José R Casar ◽  
Wilfried Philips
Keyword(s):  
Augmented Reality ◽  
Sensor Fusion ◽  
Pose Estimation ◽  
Inertial Sensor ◽  
Camera Pose Estimation ◽  
Camera Pose

Real-Time Camera Pose Estimation for Wide-Area Augmented Reality Applications

2011 ◽  
Vol 31 (3) ◽  
pp. 56-68 ◽  
Author(s):  
Tao Guan ◽  
Liya Duan ◽  
Junqing Yu ◽  
Yongjian Chen ◽  
Xu Zhang
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Pose Estimation ◽  
Wide Area ◽  
Camera Pose Estimation ◽  
Camera Pose

scholarly journals Smart Localization Using a New Sensor Association Framework for Outdoor Augmented Reality Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
F. Ababsa ◽  
I. Zendjebil ◽  
J.-Y. Didier ◽  
M. Mallem
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Inertial Sensor ◽  
Heterogeneous Sensors ◽  
The Real ◽  
Virtual Objects ◽  
Camera Pose Estimation ◽  
Markerless Tracking ◽  
3D Camera

Augmented Reality (AR) aims at enhancing our the real world, by adding fictitious elements that are not perceptible naturally such as: computer-generated images, virtual objects, texts, symbols, graphics, sounds, and smells. The quality of the real/virtual registration depends mainly on the accuracy of the 3D camera pose estimation. In this paper, we present an original real-time localization system for outdoor AR which combines three heterogeneous sensors: a camera, a GPS, and an inertial sensor. The proposed system is subdivided into two modules: the main module is vision based; it estimates the user’s location using a markerless tracking method. When the visual tracking fails, the system switches automatically to the secondary localization module composed of the GPS and the inertial sensor.

Registration using adaptive particle filter and natural features matching techniques for augmented reality systems

2009 ◽  
Vol 29 (1) ◽  
pp. 75-84
Author(s):  
Guan Tao ◽  
Li Lijun ◽  
Liu Wei ◽  
Wang Cheng
Keyword(s):  
Augmented Reality ◽  
Particle Filter ◽  
Pose Estimation ◽  
Estimation Method ◽  
Scale Invariant ◽  
Registration Method ◽  
Content Type ◽  
Camera Pose Estimation ◽  
Camera Pose ◽  
Matching Techniques

PurposeThe purpose of this paper is to provide a flexible registration method for markerless augmented reality (AR) systems.Design/methodology/approachThe proposed method distinguishes itself as follows: firstly, the method is simple and efficient, as no man‐made markers are needed for both indoor and outdoor AR applications. Secondly, an adaptation method is presented to tune the particle filter dynamically. The result is a system which can achieve tolerance to fast motion and drift during tracking process. Thirdly, the authors use the reduced scale invariant feature transform (SIFT) and scale prediction techniques to match natural features. This method deals easily with the camera pose estimation problem in the case of large illumination and visual angle changes.FindingsSome experiments are provided to validate the performance of the proposed method.Originality/valueThe paper proposes a novel camera pose estimation method based on adaptive particle filter and natural features matching techniques.

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