A stereo matching algorithm for lunar rover

In order to satisfy the requirement of navigation and localization of the lunar rover, a new quasi-dense matching algorithm based on SIFT features and region growing is presented in the paper. Firstly, the original images are preprocessed by Gauss filter and CLAHE method to weaken the effect of nois...

Full description

Saved in:
Bibliographic Details
Published in2010 2nd International Conference on Computer Engineering and Technology Vol. 1; pp. V1-586 - V1-589
Main Authors Fengping Cao, Rongben Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Educational institutions
Feature extraction
Filters
Image enhancement
Image reconstruction
Intelligent transportation systems
Intelligent vehicles
lunar rover
Moon
Navigation
quasi-dense matching
region growing
SIFT features
stereo matching
Stereo vision
Online AccessGet full text

Cover

More Information
Summary:In order to satisfy the requirement of navigation and localization of the lunar rover, a new quasi-dense matching algorithm based on SIFT features and region growing is presented in the paper. Firstly, the original images are preprocessed by Gauss filter and CLAHE method to weaken the effect of noise and contrast, then the invariant SIFT feature points in each image are extracted and reliably matched with common constraints in stereo matching, such as epipolar constraint, uniqueness constraint and continuity constraint. Finally, matched SIFT features with high accuracy are taken as the seeds from which corresponding relations propagate towards other regions of the image by best first strategy. Experimental results indicate that the algorithm performs well when used in the matching of stereo image pair of simulated lunar surface terrain environment and produces a dense disparity map with rather high accuracy.
ISBN:9781424463473
1424463475
DOI:10.1109/ICCET.2010.5485954