A slope K method for image based localization

Authors:
Hong Liu;Haitao Yu;Yuexian Zou;Zhenhua Huo
Affiliations:
Key Laboratory of Machine Perception and Intelligence, Key Laboratory of Integrated Micro-system, Shenzhen Graduate School, Peking University, China;Key Laboratory of Integrated Micro-system, Shenzhen Graduate School, Peking University, Shenzhen, China;Key Laboratory of Integrated Micro-system, Shenzhen Graduate School, Peking University, Shenzhen, China;Key Laboratory of Integrated Micro-system, Shenzhen Graduate School, Peking University, Shenzhen, China
Venue:
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Year:
2009

Citing 5
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
Global Localization and Relative Pose Estimation Based on Scale-Invariant Features

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
CSIFT: A SIFT Descriptor with Color Invariant Characteristics

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Vision-based global localization and mapping for mobile robots

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a SIFT based Slope K method which is faster and more robust than the classical SIFT in landmark based localization. First, the slope k value can be used to erase mismatched feature points (outliers) of the two compared images. Second, the y position is determined by the slope k value. Therefore, the Slope K method is able to localizes about twice as more accurate as the classical SIFT. Another advantage of the proposed method is that the number of database images needed to be matched is significantly reduced, compared to the classical SIFT. Therefore the time cost is approximate 4 times less than that of the classical SIFT.