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.

翻译：低特征环境是几何计算机视觉算法的主要短板之一。在大多数常包含低特征的人造场景中，直线可作为点的补充。本文提出一种利用点和线特征的多机器人协同视觉惯性导航系统。通过在多状态约束卡尔曼滤波框架中采用协方差交集更新，每个机器人不仅利用自身观测的点与线测量值，还利用邻居机器人观测到的公共点和公共直线特征的约束。线特征采用最近点表示法进行参数化与更新。所提算法在蒙特卡洛仿真和真实世界数据集上均得到充分验证。结果表明，点线协同的视觉惯性里程计（PL-CVIO）在低特征和丰富特征环境中均优于独立的MSCKF以及我们先前的工作CVIO。