He Yijia, Zhao Ji, Guo Yue, He Wenhao, Yuan Kui
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2018 Apr 10;18(4):1159. doi: 10.3390/s18041159.
To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point-line visual-inertial odometry (PL-VIO), a tightly-coupled monocular visual-inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.
为了解决估计相机轨迹的问题，并基于惯性测量和视觉观测构建结构化三维（3D）地图，本文提出了点线视觉惯性里程计（PL-VIO），这是一种紧密耦合的单目视觉惯性里程计系统，它利用了点和线特征。与点特征相比，线提供了关于环境的显著更多的几何结构信息。为了获得三维空间线的计算简单性和表示紧凑性，采用了线的普吕克坐标和正交表示。为了紧密且高效地融合来自惯性测量单元（IMU）和视觉传感器的信息，我们通过在滑动窗口优化框架中最小化一个成本函数来优化状态，该成本函数将预积分的IMU误差项与点和线的重投影误差项结合在一起。在公共数据集上进行的实验表明，结合点和线特征的PL-VIO方法优于几个仅使用点特征的最新VIO系统。
