Title :
A novel soft-computing technique to segment satellite images for mobile robot localization and navigation
Author :
Dogruer, C.U. ; Koku, B. ; Dolen, M.
Author_Institution :
Hacettepe Univ., Ankara
fDate :
Oct. 29 2007-Nov. 2 2007
Abstract :
Localization of mobile robots has been studied rigorously in the last decade. A number of successful approaches such as Extended Kalman Filter, Markov Localization, and Monte Carlo Localization assume that the map of the environment is originally presented to the robot. However, an important information package like the map of the environment could not be taken for granted in most real- world problems. In this study, a novel technique composed of a combination of Fuzzy C-Means and Fuzzy Neural Network methods is proposed to segment and convert a satellite image into a digital map for outdoor mobile robot applications.
Keywords :
Kalman filters; Markov processes; Monte Carlo methods; fuzzy neural nets; fuzzy set theory; image segmentation; mobile robots; nonlinear filters; Markov localization; Monte Carlo localization; extended Kalman filter; fuzzy C-means; fuzzy neural network; mobile robot localization; mobile robot navigation; satellite image segmentation; soft computing technique; Cities and towns; Fuzzy systems; Image converters; Image segmentation; Intelligent robots; Mobile robots; Monte Carlo methods; Robot sensing systems; Satellite navigation systems; Simultaneous localization and mapping;
Conference_Titel :
Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-0912-9
Electronic_ISBN :
978-1-4244-0912-9
DOI :
10.1109/IROS.2007.4399494
