Title :
Collaborative probabilistic constraint-based landmark localization
Author :
Stroupe, Ashley W. ; Balch, Tucker
Author_Institution :
Carnegie Mellon Univ., Pittsburgh, PA, USA
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;
Conference_Titel :
Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7398-7
DOI :
10.1109/IRDS.2002.1041431
