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 micro aerial vehicles in
which it is difficult to use high quality sensors and pow- erful processors
because of constraints on size and weight. In this paper, we present a
filter-based stereo visual inertial odometry that uses the Multi-State
Constraint Kalman Filter (MSCKF) [1]. Previous work on stereo visual inertial
odometry has resulted in solutions that are computationally expensive. We
demonstrate that our Stereo Multi-State Constraint Kalman Filter (S-MSCKF) is
comparable to state-of-art monocular solutions in terms of computational cost,
while providing signifi- cantly greater robustness. We evaluate our S-MSCKF
algorithm and compare it with state-of-art methods including OKVIS, ROVIO, and
VINS-MONO on both the EuRoC dataset, and our own experimental datasets
demonstrating fast autonomous flight with maximum speed of 17.5m/s in indoor
and outdoor environments. Our implementation of the S-MSCKF is available at
this https URL

本文提出了一种基于状态约束卡尔曼滤波器的、用于自主飞行的滤波器的立体视觉惯性测距法，并证明了这种方法具有与现有的单目解决方案相当的计算成本，同时提供了更高的鲁棒性。