VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
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DOI：
10.1109/tro.2018.2853729
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发表时间：
2018-08-01
影响因子：
7.8
通讯作者：
Shen, Shaojie
中科院分区：
文献类型：
--
作者：
Qin, Tong;Li, Peiliang;Shen, Shaojie
一个摄像头和一个低成本的惯性测量单元（IMU）组成了一个单目视觉惯性系统（VINS），这是用于度量六自由度（DOF）状态估计的最小传感器套件（尺寸，重量和功率）。在本文中，我们提出了VINS单：一个强大的和通用的单目视觉惯性状态估计。我们的方法从估计器初始化的稳健过程开始。采用一种基于紧耦合非线性优化的方法，通过融合预积分IMU测量值和特征观测值，获得高精度的视觉惯性里程。一个循环检测模块，结合我们的紧耦合配方，使重新定位与最小的计算。我们还执行4-DOF姿态图优化，以加强全局一致性。此外，所提出的系统可以重用的地图保存和加载它在一个有效的方式。当前和先前的地图可以通过全局姿态图优化合并在一起。我们在公开数据集和真实世界实验上验证了我们的系统的性能，并与其他最先进的算法进行了比较。我们还在微型飞行器平台上进行了机载闭环自主飞行，并将算法移植到基于iOS的演示中。我们强调，拟议的工作是一个可靠的，完整的，通用的系统，适用于不同的应用程序，需要高精度的定位。我们为PC（https：//github.com/HKUST-Aerial-Robotics/VINSMono）和iOS移动的设备（https：//github.com/HKUST-AerialRobotics/VINS-移动的）开源了我们的实现。
One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs (https://github.com/HKUST-Aerial-Robotics/VINSMono) and iOS mobile devices (https://github.com/HKUST-AerialRobotics/VINS-Mobile).