DocumentCode :
2493824
Title :
Model-based pose estimation by consensus
Author :
Jorstad, Anne ; Burlina, Philippe ; Wang, I-Jeng ; Lucarelli, Dennis ; DeMenthon, Daniel
Author_Institution :
Appl. Math. & Sci. Comput., Univ. of Maryland, College Park, MD
fYear :
2008
fDate :
15-18 Dec. 2008
Firstpage :
569
Lastpage :
574
Abstract :
We present a system for determining a consensus estimate of the pose of an object, as seen from multiple cameras in a distributed network. The cameras are pointed towards a 3D object defined by a configuration of points, which are assumed to be visible and detected in all camera images. The cameras are given a model defining the 3D configuration of these object points, but do not know which image point corresponds to which object point. Each camera estimates the pose of the object, then iteratively exchanges information with its neighbors to arrive at a common decision of the pose over the network. We consider eight variations of the consensus algorithm, and find that each converges to a more accurate result than do the individual cameras alone on average. The method exchanging 3D world coordinates penalized to agree with the input model provides the most accurate results. If bandwidth is limited, performing consensus over rotations and translations requires cameras to exchange only the six values specifying the six degrees of freedom of the object pose, and performing consensus in SE(3) using the Karcher mean is generally the best choice. We show further that interleaving pose calculation with the consensus iterations improves the final result when the image noise is large.
Keywords :
pose estimation; 3D configuration; 3D object; 3D world coordinates; Karcher mean; camera images; consensus algorithm; consensus estimate; consensus iterations; distributed network; image noise; image point; model-based pose estimation; object points; object pose; Cameras; Computer networks; Computer vision; Distributed computing; Interleaved codes; Iterative algorithms; Layout; Mathematical model; Message passing; Orbital robotics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-3822-8
Electronic_ISBN :
978-1-4244-2957-8
Type :
conf
DOI :
10.1109/ISSNIP.2008.4762050
Filename :
4762050
Link To Document :
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