Paper Info
Title
Innovative geometric pose reconstruction for marker-based single camera tracking
Abstract
Mobile augmented reality applications are in need of tracking systems which can be wearable and do not cause a high processing load, while still offering reasonable performance, robustness and accuracy. The motivation to develop yet another tracking algorithm is two-fold. Most of the existing approaches use classical optimization techniques such as the Gauss-Newton method. However, since those algorithms were developed to address general optimization problems, they do not fully exploit the structure of the pose estimation problem with its geometric constraint targets. Also, mixed reality applications demand that pose estimation be not only accurate but also robust and computationally efficient. Hence there is a need for algorithms that are as accurate as classical algorithms, yet are also globally convergent and fast enough for real-time applications. In this paper we introduce a new iterative geometric method for pose estimation from four co-planar points and we present the current status of PTrack, an infrared marker-based single camera tracking system benefiting from this approach. Our novel pose estimation algorithm identifies possible labels composed of retro-reflective markers in a 2D post-processing using a divide-and-conquer strategy to segment the camera's image space and attempts an iterative geometric 3D reconstruction of position and orientation in camera space. Tracking results are made available to applications through OpenTracker [OpenTracker 2006] framework. To analyse tracking accuracy and precision, we built a generic test-bed and compared PTrack to ARToolKit [Kato and Billinghurst 1999; Kato et al. 2000], one of the most wide-spread low-cost tracking solutions. Results show that our tracking system achieves competitive accuracy levels better than ARToolKit and close to commercial systems, while being highly stable and affordable.
Year
DOI
Venue
2006
10.1145/1128923.1128962
VRCIA
Keywords
Field
DocType
estimation problem,competitive accuracy level,geometric constraint target,wide-spread low-cost tracking solution,tracking system,infrared marker-based single camera,camera space,mixed reality applications demand,marker-based single camera tracking,estimation algorithm,tracking algorithm,pose estimation,test bed,optimization problem,virtual reality,divide and conquer,augmented reality,infrared,3d reconstruction,mixed reality
Computer vision,Computer science,Tracking system,3D pose estimation,Pose,Robustness (computer science),Augmented reality,Artificial intelligence,Mixed reality,Optimization problem,3D reconstruction
Conference
ISBN
Citations 
PageRank 
1-59593-324-7
3
0.49
References 
Authors
16
4
Name
Order
Citations
PageRank
Pedro Santos18021.90
Andre Stork210711.13
Alexandre Buaes3232.68
Joaquim Jorge413111.06