Stereo Visual Odometry with Light and Adaptive Feature Tracking | |
Xin Huang1,2; Shuming Tang1; Lifu Zhang1,2; Haibing Zhu1; Qingxiu Du1 | |
2019-07 | |
会议名称 | International Conference on Image, Vision and Computing |
会议日期 | 2019.7.5-2019.7.7 |
会议地点 | 中国厦门 |
摘要 | 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. |
关键词 | stereo visual odomerty feature tracking VINS-Fusion bi-circular check adaptive feature selection |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39219 |
专题 | 智能制造技术与系统研究中心 |
通讯作者 | Shuming Tang |
作者单位 | 1.中科院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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V167-6.15.pdf(781KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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