论文标题
利用平面规律的点线视觉惯性进程仪
Leveraging Planar Regularities for Point Line Visual-Inertial Odometry
论文作者
论文摘要
使用单眼视觉惯性探测器（VIO）系统，可以同时估算3D点云和摄像机运动。由于纯稀疏3D点提供了环境的无结构表示，因此从稀疏点产生3D网格可以进一步对环境拓扑进行建模并产生密集的映射。为了提高3D网格生成和本地化的准确性，我们提出了一个紧密耦合的单眼VIO系统PLP-VIO，该系统利用点特征和线路特征以及平面规律性。共平面性约束用于利用其他结构信息，以更准确地估计状态估计器中的3D点和空间线。为了稳健地检测平面和3D网格，我们将两个线特征与检测方法中的点特征结合在一起。在合成数据和公共数据集上验证了所提出方法的有效性，并将其与其他最先进的算法进行了比较。
With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless representation of the environment, generating 3D mesh from sparse points can further model the environment topology and produce dense mapping. To improve the accuracy of 3D mesh generation and localization, we propose a tightly-coupled monocular VIO system, PLP-VIO, which exploits point features and line features as well as plane regularities. The co-planarity constraints are used to leverage additional structure information for the more accurate estimation of 3D points and spatial lines in state estimator. To detect plane and 3D mesh robustly, we combine both the line features with point features in the detection method. The effectiveness of the proposed method is verified on both synthetic data and public datasets and is compared with other state-of-the-art algorithms.
