The visual-inertial navigation system using a single camera and IMU requires an accurate initialization without increasing the processing cost and complexity for real-time deployment. The processing cost in the existing solutions can be traced to the gyroscopic bias estimation using 1) Closed-form solutions (Martinelli, Int. J. Comput. Vis. 106(2), 138–152, 2014; Kaiser et al., IEEE Robot. Autom. Lett. 2(1), 18–25, 2017 and 2) Loosely coupled schemes using visual-inertial alignment (Mur-Artal and Tardós IEEE Robot. Autom. Lett. 2(2), 796–803 2017); Qin, IEEE Trans. Robot. 34(4), 1004–1020 2018). The complexity arises because of the non-linear nature of the system to estimate the gyro bias, which is solved either by directly solving the non-linear, non-convex problem or by decoupling the vision and IMU measurements using linear models. The termination conditions are based on condition number or covariance of the estimated variables, which varies from one experiment to another. The present paper seeks to improve the closed-form solution with higher accuracy and less processing cost per frame. The proposed method separates the gyroscope bias estimation from the closed-form solution using tightly coupled but linear models with reduced number of variables in the closed-form solution. This paper also addresses the problem inherent to the closed-form solutions, which requires sufficient motion in the initialization window with a minimum number of common features. Towards this, a novel method of propagating the past information into the present initialization window is presented. This reduces the total processing cost per frame by limiting the initialization window to 10 frames ≈ 1s without compromising the motion inside the window. We also present a common and intuitive termination criteria which is independent from the experiment scenario. This helps to increase the robustness in the initialization by removing erroneous solutions. The proposed method is evaluated with EuRoC Micro Aerial Vehicle (MAV) dataset (Burri et al. 2016) sequences. We compare the proposed method with a recently proposed loosely coupled method, which shows the improved accuracy, processing cost and robustness in the initialization.

使用单个摄像头和IMU的视觉惯性导航系统需要精确的初始化，而不会增加实时部署的处理成本和复杂性。在现有的解决方案的处理成本，可以追溯到使用1）闭合形式解陀螺偏置估计（Martinelli的诠释J. COMPUT，可见。106，138-152，2014（2）; Kaiser等，IEEE。机器人。奥波快报。2（1），18-25，2017年和2）使用视觉惯性对准松耦合方案（穆尔-ARTAL和TardósIEEE机器人。奥波快报。2（2），796-803 2017） ; 秦，IEEE Trans。机器人。34（4），1004–1020 2018）。复杂性的产生是由于估算陀螺仪偏置的系统的非线性特性，可通过直接解决非线性，非凸问题或通过使用线性模型解耦视觉和IMU测量来解决。终止条件基于条件数或估计变量的协方差，它们在一个实验与另一个实验之间会有所不同。本文试图以更高的精度和更少的每帧处理成本来改进封闭形式的解决方案。所提出的方法使用紧密耦合但线性模型的陀螺仪偏差估计值与闭合形式的解决方案分离，该模型紧密耦合但线性模型具有减少的闭合形式的变量数量。本文还解决了封闭式解决方案固有的问题，这需要在初始化窗口中以最少数量的常用功能进行足够的移动。为此，提出了一种将过去的信息传播到当前的初始化窗口中的新颖方法。通过将初始化窗口限制为10帧≈1s，而不会损害窗口内部的运动，从而降低了每帧的总处理成本。我们还提出了独立于实验场景的通用且直观的终止标准。通过消除错误的解决方案，这有助于提高初始化的鲁棒性。用EuRoC微型飞行器（MAV）数据集（Burri et al.2016）序列评估提出的方法。我们将提出的方法与最近提出的松散耦合方法进行了比较，结果表明该方法的准确性有所提高，