Title
Using egocentric vision to achieve robust inertial body tracking under magnetic disturbances
Abstract
In the context of a smart user assistance system for industrial manipulation tasks it is necessary to capture motions of the upper body and limbs of the worker in order to derive his or her interactions with the task space. While such capturing technology already exists, the novelty of the proposed work results from the strong requirements of the application context: The method should be flexible and use only on-body sensors, work accurately in industrial environments that suffer from severe magnetic disturbances, and enable consistent registration between the user body frame and the task space. Currently available systems cannot provide this. This paper suggests a novel egocentric solution for visual-inertial upper-body motion tracking based on recursive filtering and model-based sensor fusion. Visual detections of the wrists in the images of a chest-mounted camera are used as substitute for the commonly used magnetometer measurements. The on-body sensor network, the motion capturing system, and the required calibration procedure are described and successful operation is shown in a real industrial environment.
Year
DOI
Venue
2011
10.1109/ISMAR.2011.6092528
Mixed and Augmented Reality
Keywords
Field
DocType
on-body sensor,available system,industrial manipulation task,model-based sensor fusion,proposed work result,on-body sensor network,robust inertial body tracking,real industrial environment,task space,magnetic disturbance,application context,egocentric vision,industrial environment,magnetometers,motion tracking,motion capture,tracking,signal processing,magnetic resonance image,magnetic resonance imaging,sensor fusion,sensors
Inertial frame of reference,Computer vision,User assistance,Computer science,Visual sensor network,Filter (signal processing),Tracking system,Sensor fusion,Artificial intelligence,Wireless sensor network,Match moving
Conference
ISSN
ISBN
Citations 
1554-7868
978-1-4577-2184-7
8
PageRank 
References 
Authors
0.57
11
3
Name
Order
Citations
PageRank
Gabriele Bleser124127.76
Gustaf Hendeby221621.37
Markus Miezal3505.74