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Proceedings Paper

Robust stereo visual odometry based on points and lines
Author(s): Jhua Zhang; Youjie Zhou; Yan Zhao; Yuan Xue; Lin Zhang; Aidi Zhao; Wei He
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Paper Abstract

Due to the difference of scenes, the uncertainty of detection for line features in stereo visual odometer reduces the accuracy of initialization for line features, and the point-line fusion visual odometry is easy to cause uneven distribution of features in feature extraction and matching process. The problems, which will lead to incomplete feature extraction and thus inaccuracy of pose estimation. In order to solve the problems, we design a stereo visual odometry system called PLWM-VO, which adaptively allocates the weights of the point features and the endpoints of line features in each frame through the adaptive weight assignment model. Firstly, the improved SAD algorithm is used to accurately detect the endpoints of line features, through which improves the registration accuracy of line features’ endpoints in the left and right, and improves the accuracy of line features in the initialization process. Secondly, the adaptive weight assignment model based on region partition assigns weights to point features and endpoints of line features. In this process, the effect of uneven feature distribution on the accuracy of pose estimation is reduced by region growth. Finally, experiment results on the open dataset shows that the proposed algorithm exhibits higher precision and stability than the state-of-the-art methods, which improves the positioning accuracy of our designed stereo visual odometry system.

Paper Details

Date Published: 31 January 2020
PDF: 7 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 1142724 (31 January 2020); doi: 10.1117/12.2552217
Show Author Affiliations
Jhua Zhang, Hebei Univ. of Technology (China)
Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Youjie Zhou, Hebei Univ. of Technology (China)
Yan Zhao, Beijing Institute of Technology (China)
Yuan Xue, Hebei Univ. of Technology (China)
Lin Zhang, Hebei Univ. of Technology (China)
Aidi Zhao, Hebei Univ. of Technology (China)
Wei He, Hebei Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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