Liu Yi, Chen Zhong, Zheng Wenjuan, Wang Hao, Liu Jianguo
National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Beijing Aerospace Automatic Control Institute, Beijing 100854, China.
Sensors (Basel). 2017 Nov 14;17(11):2613. doi: 10.3390/s17112613.
In this paper, we propose a new visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm. With the tightly coupled sensor fusion of a global shutter monocular camera and a low-cost Inertial Measurement Unit (IMU), this algorithm is able to achieve robust and real-time estimates of the sensor poses in unknown environment. To address the real-time visual-inertial fusion problem, we present a parallel framework with a novel IMU initialization method. Our algorithm also benefits from the novel IMU factor, the continuous preintegration method, the vision factor of directional error, the separability trick and the robust initialization criterion which can efficiently output reliable estimates in real-time on modern Central Processing Unit (CPU). Tremendous experiments also validate the proposed algorithm and prove it is comparable to the state-of-art method.
在本文中，我们提出了一种新的视觉惯性同步定位与地图构建（SLAM）算法。通过全局快门单目相机与低成本惯性测量单元（IMU）的紧密耦合传感器融合，该算法能够在未知环境中实现对传感器位姿的稳健实时估计。为了解决实时视觉惯性融合问题，我们提出了一个具有新颖IMU初始化方法的并行框架。我们的算法还受益于新颖的IMU因子、连续预积分方法、方向误差视觉因子、可分离技巧以及稳健初始化准则，这些能够在现代中央处理器（CPU）上高效实时地输出可靠估计。大量实验也验证了所提算法，并证明它与当前的先进方法相当。
