A Real-Time Stereo Visual-Inertial SLAM System Based on Point-and-Line Features

Simultaneous localization and mapping (SLAM) is widely used in various fields, such as unmanned driving, robotics, and VR. The SLAM system with multiple landmarks is also a research hotspot currently. In this study, a real-time stereo visual-inertial SLAM system based on point-and-line features is proposed; the system is studied based on VINS-fusion. It improves the front-end process of VINS fusion. First, we proposed an IMU-assisted hierarchical grid optical flow tracking method that can more accurately and quickly track points between frames. Second, we add line features on the basis of existing point features. To match line features in real time, we only select the best line features to participate in optimization. We combine line segments by their geometric relationship to reduce line segment splitting and representation redundancy in the LSD algorithm. We further predict line features by IMU-assisted optical flow tracking to achieve high precision matching. In the back-end optimization process, we used a redundant structure to avoid the failure of stereo constraints in a highly dynamic environment. The proposed method outperforms the state-of-the-art methods (VINS-fusion and PL-VINS) on the EuRoC MAV dataset. The system also achieve good performance in a real environment.