An Online Initialization and Self-Calibration Method for Stereo Visual-Inertial Odometry

Abstract

Most online initialization and self-calibration methods for visual-inertial odometry (VIO) are only able to estimate the extrinsic parameters (orientation and translation) between one camera and inertial measurement unit (IMU) pair. They are not applicable to stereo VIO where both camera-IMU and camera-camera pairs exist. In this article, we address the issue by taking advantage of the geometric constraints among the multiple sensors. An online method is proposed to estimate the initial values of velocity, gravity, IMU biases, and simultaneously calibrate the extrinsic parameters of camera-camera and camera-IMU pairs for bootstrapping a smoothing-based stereo VIO system. The method includes a three-step process to incrementally solve several linear equations in a coarse-to-fine manner. It back-propagates historically estimated results to update weight factors and remove outliers, and employs a convergence criterion to monitor and terminate the process. It also includes an optional global optimization for further refinement. The method is evaluated in terms of accuracy, robustness, convergence, consistency, and tunable parameters using both simulated and public datasets. Experimental results show that the proposed method can accurately estimate the initial values and the extrinsic parameters.