TrackingExpert: A Versatile Tracking Toolbox for Augmented Reality

This paper presents a toolbox for rigid object tracking with a focus on augmented reality applications. Augmented reality relies on tracking to superimpose virtual objects on physical objects. Object tracking is usually based on registration and pose estimation techniques. Many different approaches have already been introduced. Our research focuses on tracking for application areas such as assembly assistance and the most promising candidate is rigid object tracking based on point cloud registration. Our work advances the robustness of point cloud-based tracking as well as the performance. One product of our research is our tracking tool TrackingExpert, which integrates all our research outcomes into one versatile software package. This paper introduces TrackingExpert covering functional areas such as the registration, visualizations, and experiment support. We also highlight several aspects which facilitate data analysis and ease our research.

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Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality

Volume 1B: 34th Computers and Information in Engineering Conference ◽

10.1115/detc2014-35319 ◽

2014 ◽

Cited By ~ 1

Author(s):

Francely Franco Bermudez ◽

Christian Santana Diaz ◽

Sheneeka Ward ◽

Rafael Radkowski ◽

Timothy Garrett ◽

...

Keyword(s):

Augmented Reality ◽

Object Tracking ◽

Real Time ◽

Virtual Object ◽

Interest Points ◽

Rigid Object ◽

Binary Descriptors ◽

Rigid Objects ◽

Natural Feature ◽

Feature Descriptors

This paper presents a comparison of natural feature descriptors for rigid object tracking for augmented reality (AR) applications. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natural feature tracking (NFT) is one approach for computer vision-based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descriptors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different natural feature descriptors (SIFT, SURF, FREAK, ORB) and their capability to track rigid objects. Rigid objects need robust descriptors since they need to describe the objects in a 3D space. AR applications are also real-time application, thus, fast feature matching is mandatory. FREAK and ORB are binary descriptors, which promise a higher performance in comparison to SIFT and SURF. We deployed a test in which we match feature descriptors to artificial rigid objects. The results indicate that the SIFT descriptor is the most promising solution in our addressed domain, AR-based assembly training.

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

Journal of Robotics ◽

10.1155/2012/634758 ◽

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.

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Pose estimation of rigid object in point cloud

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei.2016.7852801 ◽

2016 ◽

Author(s):

Zongming Liu ◽

Guodong Liu ◽

Jianxun Li ◽

Dong Ye

Keyword(s):

Pose Estimation ◽

Point Cloud ◽

Rigid Object

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Object Tracking With a Range Camera for Augmented Reality Assembly Assistance

Journal of Computing and Information Science in Engineering ◽

10.1115/1.4031981 ◽

2016 ◽

Vol 16 (1) ◽

Cited By ~ 16

Author(s):

Rafael Radkowski

Keyword(s):

Augmented Reality ◽

Object Tracking ◽

Point Cloud ◽

Sampling Methods ◽

Point Clouds ◽

Reference Object ◽

Key Factors ◽

Tracking Method ◽

Uniform Sampling ◽

Feature Descriptors

This paper introduces a 3D object tracking method for an augmented reality (AR) assembly assistance application. The tracking method relies on point clouds; it uses 3D feature descriptors and point cloud matching with the iterative closest points (ICP) algorithm. The feature descriptors identify an object in a point cloud; ICP align a reference object with this point cloud. The challenge is to achieve high fidelity while maintaining camera frame rates. The point cloud and reference object sampling density are one of the key factors to meet this challenge. In this research, three-point sampling methods and two-point cloud search algorithms were compared to assess their fidelity when tracking typical products of mechanical engineering. The results indicate that a uniform sampling maintains the best fidelity at camera frame rates.

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