A terrain classification method for UGV autonomous navigation based on SURF

The ability to navigate autonomously in off-road terrain is critical technology needed for unmanned ground vehicle (UGV). This paper presents a vision-based off-road terrain classification method that is robust despite environmental variation caused by weather changes. In order to cope with an overa...

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Bibliographic Details
Main Authors: Seung-Youn Lee, Dong-Min Kwak
Format: Conference Proceeding
Language:English
Subjects:
autonomous navigation
Brightness
Classification algorithms
Feature extraction
Image color analysis
Robustness
Support vector machine classification
SURF
terrain classification
Training
UGV
wavelet features
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Summary:The ability to navigate autonomously in off-road terrain is critical technology needed for unmanned ground vehicle (UGV). This paper presents a vision-based off-road terrain classification method that is robust despite environmental variation caused by weather changes. In order to cope with an overall image brightness variation, we use speeded-up robust features (SURF), and neural network classifier. Experimental results for real off-road images show that proposed method has a better performance than wavelet based one especially in case of large brightness variation.
DOI:10.1109/URAI.2011.6145981