Mobile robot navigation using SURF features

Authors:
Masayoshi Tabuse;Dai Nakai
Affiliations:
Graduate School of Life and Environmental Science, Kyoto Prefectural University, Kyoto, Japan;Kyoto Prefectural Subaru High School, Kyoto, Japan
Venue:
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Year:
2010

Citing 3
Cited 0

Object Recognition from Local Scale-Invariant Features

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose SURF feature based approach for mobile robot navigation. In this approach, we capture environmental images by using omni-directional camera and extract features of these images by using SURF. We treat these features as landmarks and estimate robot self-location and direction of motion. SURF features are invariant under scale change and rotation and robust under image noise, change in light condition and change view point. Therefore, SURF features are appropriate ones for robot self-location estimation and navigation.