  This paper presents a dual stage EKF (Extended Kalman Filter)-based algorithm
for the real-time and robust stereo VIO (visual inertial odometry). The first
stage of this EKF-based algorithm performs the fusion of accelerometer and
gyroscope while the second performs the fusion of stereo camera and IMU. Due to
the sufficient complementary characteristics between accelerometer and
gyroscope as well as stereo camera and IMU, the dual stage EKF-based algorithm
can achieve a high precision of odometry estimations. At the same time, because
of the low dimension of state vector in this algorithm, its computational
efficiency is comparable to previous filter-based approaches. We call our
approach DS-VIO (dual stage EKFbased stereo visual inertial odometry) and
evaluate our DSVIO algorithm by comparing it with the state-of-art approaches
including OKVIS, ROVIO, VINS-MONO and S-MSCKF on the EuRoC dataset. Results
show that our algorithm can achieve comparable or even better performances in
terms of the RMS error

Computer Vision and Pattern Recognition