DocumentCode
2371340
Title
Collaborative probabilistic constraint-based landmark localization
Author
Stroupe, Ashley W. ; Balch, Tucker
Author_Institution
Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
1
fYear
2002
fDate
2002
Firstpage
447
Abstract
We present an efficient probabilistic method for localization using landmarks that supports individual robot and multi-robot collaborative localization. The approach, based on the Kalman-Bucy filter, reduces computation by treating different types of landmark measurements (for example, range and bearing) separately. Our algorithm has been extended to perform two types of collaborative localization for robot teams. Results illustrating the utility of the approach in simulation and on a real robot are presented.
Keywords
Gaussian distribution; cooperative systems; filtering theory; mobile robots; multi-robot systems; object recognition; path planning; robot vision; Gaussian distribution; Kalman-Bucy filter; collaborative localization; collaborative probabilistic; constraint-based localization; landmark localization; mobile robots; robot teams; robot vision; Collaboration; Computational modeling; Filters; Gaussian distribution; Global Positioning System; High performance computing; Monte Carlo methods; Robot localization; Robot sensing systems; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on
Print_ISBN
0-7803-7398-7
Type
conf
DOI
10.1109/IRDS.2002.1041431
Filename
1041431
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