CASIA OpenIR  > 智能制造技术与系统研究中心
Stereo Visual Odometry with Light and Adaptive Feature Tracking
Xin Huang1,2; Shuming Tang1; Lifu Zhang1,2; Haibing Zhu1; Qingxiu Du1
2019-07
Conference NameInternational Conference on Image, Vision and Computing
Conference Date2019.7.5-2019.7.7
Conference Place中国厦门
Abstract

Localization technology plays a key role in autonomous driving. Stereo visual odometry is a meaningful visual localization method to estimate the pose of autonomous vehicles. VINS-Fusion provides a state-of-the-art stereo visual odometry with Kanade-Lucas-Tomasi (KLT) tracker to achieve fast feature tracking. However, KLT tracker is prone to fall into local minima in urban environments due to illumination changes and large displacements, leading to catastrophic cumulative drift over time. Aiming to solve this problem, we present a light and adaptive feature tracking technique for VINS-Fusion to get a reliable set of measurements for pose estimation. First, a disparity constraint is incorporated into left-right check to refine the measurements. Next, we propose a light bi-circular check to further remove outliers, which has high efficiency with the ingenious design. Additionally, an adaptive strategy for feature selection is proposed to dynamically balance the quantity and quality of the measurements. Experiments demonstrate that our method outperforms VINS-Fusion by producing more accurate pose estimation with 20% speedup on the KITTI odometry benchmark.

 
Keywordstereo visual odomerty feature tracking VINS-Fusion bi-circular check adaptive feature selection
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39219
Collection智能制造技术与系统研究中心
Corresponding AuthorShuming Tang
Affiliation1.中科院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Xin Huang,Shuming Tang,Lifu Zhang,et al. Stereo Visual Odometry with Light and Adaptive Feature Tracking[C],2019.
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