Stereo Visual Inertial Odometry with Online Baseline Calibration

Abstract

Stereo-vision devices have rigorous requirements for extrinsic parameter calibration. In Stereo Visual Inertial Odometry (VIO), inaccuracy in or changes to camera extrinsic parameters may lead to serious degradation in estimation performance. In this manuscript, we propose an online calibration method for stereo VIO extrinsic parameters correction. In particular, we focus on Multi-State Constraint Kalman Filter (MSCKF [1]) framework to implement our method. The key component is to formulate stereo extrinsic parameters as part of the state variables and model the Jacobian of feature reprojection error with respect to stereo extrinsic parameters as sub-block of update Jacobian. Therefore we can estimate stereo extrinsic parameters simultaneously with inertial measurement unit (IMU) states and camera poses. Experiments on EuRoC dataset and real-world outdoor dataset demonstrate that the proposed algorithm produce higher positioning accuracy than the original S-MSCKF [2], and the noise of camera extrinsic parameters are self-corrected within the system.