Robust Visual-Inertial Navigation System for Low Precision Sensors under Indoor and Outdoor Environments
Robust Visual-Inertial Navigation System for Low Precision Sensors under Indoor and Outdoor Environments
复制标题
适用于室内和室外环境下低精度传感器的鲁棒视觉惯性导航系统
DOI：
10.3390/rs13040772
复制
发表时间：
2021-02-01
期刊：
影响因子：
5
通讯作者：
Li, Zengke
中科院分区：
文献类型：
--
作者：
Xu, Changhui;Liu, Zhenbin;Li, Zengke
同时定位与地图构建（SLAM）一直是机器人导航领域的研究热点，也是近年来的研究热点。由于基于视觉传感器的SLAM系统易受环境光照和纹理的影响，单目SLAM系统仍然存在初始尺度模糊的问题。单目摄像机与惯性测量单元（IMU）的融合可以有效解决尺度模糊问题，提高系统的鲁棒性，实现更高的定位精度。基于单目视觉-惯性导航系统（VINS-mono），设计了一种新的初始化方案，在初始化过程中将加速度偏差作为一个变量进行计算，使其能够应用于低成本的IMU传感器。此外，为了获得更好的初始化精度，采用基于特征点的视觉匹配定位方法辅助初始化过程。初始化过程完成后，切换到光流跟踪视觉定位模式，降低了计算复杂度。该方法融合了特征点法和光流法的优点。本文首次同时使用特征点法和光流法，在低成本传感器下，定位精度和鲁棒性的综合性能较好。通过EuRoc数据集和校园环境进行的实验表明，通过初始化过程获得的初始值可以有效地用于启动非线性视觉-惯性状态估计器，改进的VINS-mono的定位精度比VINS-mono提高了约10%。
Simultaneous Localization and Mapping (SLAM) has always been the focus of the robot navigation for many decades and becomes a research hotspot in recent years. Because a SLAM system based on vision sensor is vulnerable to environment illumination and texture, the problem of initial scale ambiguity still exists in a monocular SLAM system. The fusion of a monocular camera and an inertial measurement unit (IMU) can effectively solve the scale blur problem, improve the robustness of the system, and achieve higher positioning accuracy. Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors. Besides, in order to obtain better initialization accuracy, visual matching positioning method based on feature point is used to assist the initialization process. After the initialization process, it switches to optical flow tracking visual positioning mode to reduce the calculation complexity. By using the proposed method, the advantages of feature point method and optical flow method can be fused. This paper, the first one to use both the feature point method and optical flow method, has better performance in the comprehensive performance of positioning accuracy and robustness under the low-cost sensors. Through experiments conducted with the EuRoc dataset and campus environment, the results show that the initial values obtained through the initialization process can be efficiently used for launching nonlinear visual-inertial state estimator and positioning accuracy of the improved VINS-mono has been improved by about 10% than VINS-mono.