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An improved binocular visual odometry algorithm based on the Random Sample Consensus in visual navigation systems

Qian Sun (College of Information and Communication Engineering, Harbin Engineering University, Harbin, China)
Ming Diao (College of Information and Communication Engineering, Harbin Engineering University, Harbin, China)
Yibing Li (College of Information and Communication Engineering, Harbin Engineering University, Harbin, China)
Ya Zhang (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 19 June 2017

Abstract

Purpose

The purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems.

Design/methodology/approach

The authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched feature pairs are removed by using the RANSAC method to reduce the interference of error matchings.

Findings

The performance of this new algorithm has been examined by an actual experiment data. The results shown that not only the robustness of feature detection and matching can be enhanced but also the positioning error can be significantly reduced by utilizing this novel binocular visual odometry algorithm. The feasibility and effectiveness of the proposed matching method and the improved binocular visual odometry algorithm were also verified in this paper.

Practical implications

This paper presents an improved binocular visual odometry algorithm which has been tested by real data. This algorithm can be used for outdoor vehicle navigation.

Originality/value

A binocular visual odometer algorithm based on FAST extractor and RANSAC methods is proposed to improve the positioning accuracy and robustness. Experiment results have verified the effectiveness of the present visual odometer algorithm.

Keywords

Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 51509049 and 51679047), Postdoctoral Foundation of Heilongjiang Province (No. LBH-Z16044).

Conflicts of Interest: The authors declare no conflict of interest.

Citation

Sun, Q., Diao, M., Li, Y. and Zhang, Y. (2017), "An improved binocular visual odometry algorithm based on the Random Sample Consensus in visual navigation systems", Industrial Robot, Vol. 44 No. 4, pp. 542-551. https://doi.org/10.1108/IR-11-2016-0280

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited