Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
This work addresses the problem of enabling robust and efficient state estimation for micro aerial vehicles with size and weight constraints, representing an incremental improvement over existing methods.
The paper tackles the challenge of improving computational efficiency and robustness for stereo visual inertial odometry in fast autonomous flight, demonstrating that their S-MSCKF method achieves comparable computational cost to state-of-art monocular solutions while providing significantly greater robustness, with evaluations on datasets including fast flight up to 17.5m/s.
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 https://github.com/KumarRobotics/msckf_vio.