DocumentCode :
3179049
Title :
Experiments with robust estimation techniques in real-time robot vision
Author :
Malis, Ezio ; Marchand, Eric
Author_Institution :
INRIA, Sophia-Antipolis
fYear :
2006
fDate :
Oct. 2006
Firstpage :
223
Lastpage :
228
Abstract :
The goal of this paper is to present an overview of robust estimation techniques with a special focus on robotic vision applications. In this particular context, constraints due computation time have to be considered in the choice of the estimation algorithm. Among the numerous techniques proposed in the literature to obtained robust estimation we have, not being exhaustive, Hough transform, RANSAC (Random Sample Consensus), the LMedS (Least Median of Squares), the M-estimators, etc. In this overview, we describe these various approaches in the light of a simple example. Finally, we illustrate the use of robust estimation techniques by various examples in real-time robot vision
Keywords :
Hough transforms; estimation theory; least mean squares methods; robot vision; Hough transform; least median of squares; random sample consensus; real-time robot vision; robust estimation; Cameras; Convergence; Data mining; Electric breakdown; Intelligent robots; Parameter estimation; Robot vision systems; Robustness; Solid modeling; Time factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-0259-X
Electronic_ISBN :
1-4244-0259-X
Type :
conf
DOI :
10.1109/IROS.2006.282572
Filename :
4058722
Link To Document :
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