An Efficient Approach to Initialization of Visual-Inertial Navigation System using Closed-Form Solution for Autonomous Robots

Abstract

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 Tardos 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.