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This paper presents a dual stage EKF (Extended Kalman Filter)-based algorithmfor the real-time and robust stereo VIO (visual inertial odometry). The firststage of this EKF-based algorithm performs the fusion of accelerometer andgyroscope while the second performs the fusion of stereo camera and IMU. Due tothe sufficient complementary characteristics between accelerometer andgyroscope as well as stereo camera and IMU, the dual stage EKF-based algorithmcan achieve a high precision of odometry estimations. At the same time, becauseof the low dimension of state vector in this algorithm, its computationalefficiency is comparable to previous filter-based approaches. We call ourapproach DS-VIO (dual stage EKFbased stereo visual inertial odometry) andevaluate our DSVIO algorithm by comparing it with the state-of-art approachesincluding OKVIS, ROVIO, VINS-MONO and S-MSCKF on the EuRoC dataset. Resultsshow that our algorithm can achieve comparable or even better performances interms of the RMS error

DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF