Improving Visual- Inertial Odometry with Robust Outlier Rejection and Loop Closure

Abstract

In order to obtain more accurate pose estimation, the visual-inertial odometry (VIO) system with outlier rejection and loop closure is proposed in this paper. Considering that feature matching is an important part in the front-end of the VIO system, its accuracy will affect the performance of the entire system. So we introduce an outlier rejection method of the grid-based motion statistics (GMS) algorithm to the VIO system. And for more robust feature correspondence and better camera pose estimation, we propose an improved GMS method to eliminate the mismatched points. Besides, we adopt the loop closure strategy to correct the cumulative error of the VIO system. Finally, we estimate the camera pose, velocity and IMU bias simultaneously by minimizing the loss function which contains reprojection error and IMU error. A large number of experiments on EuRoC demonstrate that the proposed method outperforms the advanced VIO system ROVIO and is comparable to the state-of-the-art VIO system OKVIS.