Novel Method for Monocular Vision Based Mobile Robot Localization

Author

Maohai, Li ; Bingrong, Hong ; Ronghua, Luo

Author_Institution

Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol.

Volume

2

fYear

2006

fDate

3-6 Nov. 2006

Firstpage

949

Lastpage

954

Abstract

A robust environment map with 3D spatial natural landmarks that facilitates monocular vision based mobile robot for global localization is built. The highly distinctive multi-dimensional vector descriptors associated with the features extracted through scale invariant feature transform (SIFT) can be robustly matched despite changes in illumination, scale and viewpoint. The landmarks are 3D restructured with the matching image feature pairs obtained through the KD-tree based nearest search approach. Novel RANSAC approach based on generic optimization for global localization is presented. Experiments on the robot Pioneer3 with monocular vision in our real indoor environment show that our method is of high precision

Keywords

feature extraction; image matching; image reconstruction; mobile robots; robot vision; robust control; transforms; trees (mathematics); 3D spatial natural landmark; KD-tree based nearest search; RANSAC approach; feature extraction; generic optimization; global localization; image matching; mobile robot localization; monocular vision; multidimensional vector descriptors; robot Pioneer3; robust environment map; scale invariant feature transform; Computer science; Feature extraction; Histograms; Indoor environments; Mobile robots; Principal component analysis; Robot sensing systems; Robot vision systems; Robustness; Sonar;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.295401

Filename

4076097