PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry
PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry
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DOI：
10.1109/ccta54093.2023.10253266
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Yanyu Zhang;Pengxiang Zhu;Wei Ren
中科院分区：
文献类型：
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作者：
Yanyu Zhang;Pengxiang Zhu;Wei Ren
低特征环境是几何计算机视觉（CV）算法的主要致命弱点之一。在大多数通常具有低特征的人工构建场景中，线可以被视为点的补充。提出了一种基于点和线特征的多机器人协同视觉惯性导航系统。通过利用协方差交叉（CI）更新内的多状态约束卡尔曼滤波器（MSCKF）的框架，每个机器人不仅利用自己的点和线的测量，但也限制其邻居观察到的公共点和公共线的功能。通过使用最近点表示来参数化和更新线要素。所提出的算法在蒙特-卡罗模拟和真实世界的数据集进行了广泛的验证。结果表明，点-线合作视觉-惯性里程计（PL-CVIO）优于独立的MSCKF和我们以前的工作CVIO在低特征和丰富的特征环境。
Low-feature environments are one of the main Achilles’ heels of geometric computer vision (CV) algorithms. In most human-built scenes often with low features, lines can be considered complements to points. In this paper, we present a multi-robot cooperative visual-inertial navigation system (VINS) using both point and line features. By utilizing the covariance intersection (CI) update within the multi-state constraint Kalman filter (MSCKF) framework, each robot exploits not only its own point and line measurements, but also constraints of common point and common line features observed by its neighbors. The line features are parameterized and updated by utilizing the Closest Point representation. The proposed algorithm is validated extensively in both Monte-Carlo simulations and a real-world dataset. The results show that the point-line cooperative visual-inertial odometry (PL-CVIO) outperforms the independent MSCKF and our previous work CVIO in both low-feature and rich-feature environments.