A novel soft-computing technique to segment satellite images for mobile robot localization and navigation

Date

2007-11-02

Author

DOĞRUER, CAN ULAŞ
Koku, Ahmet Buğra
Dölen, Melik

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 realworld 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.

Subject Keywords

Image segmentation, Mobile robots, Satellite navigation systems, Simultaneous localization and mapping, Robot sensing systems, Intelligent robots, Cities and towns, Image converters, Fuzzy systems, Monte Carlo methods

URI

https://hdl.handle.net/11511/42729

DOI

https://doi.org/10.1109/iros.2007.4399494

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

C. U. DOĞRUER, A. B. Koku, and M. Dölen, “A novel soft-computing technique to segment satellite images for mobile robot localization and navigation,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42729.