Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight

Abstract

In recent years, vision-aided inertial odometry for state estimation has matured significantly. However, we still encounter challenges in terms of improving the computational efficiency and robustness of the underlying algorithms for applications in autonomous flight with microaerial vehicles, in which it is difficult to use high-quality sensors and powerful processors because of constraints on size and weight. In this letter, we present a filter-based stereo visual inertial odometry that uses the multistate constraint Kalman filter. Previous work on the stereo visual inertial odometry has resulted in solutions that are computationally expensive. We demonstrate that our stereo multistate constraint Kalman filter (S-MSCKF) is comparable to state-of-the-art monocular solutions in terms of computational cost, while providing significantly greater robustness. We evaluate our S-MSCKF algorithm and compare it with state-of-the-art methods including OKVIS, ROVIO, and VINS-MONO on both the EuRoC dataset and our own experimental datasets demonstrating fast autonomous flight with a maximum speed of 17.5 m/s in indoor and outdoor environments. Our implementation of the S-MSCKF is available at https://github.com/KumarRobotics/msckf_vio.