摘要 : Visual-inertial navigation system (VINS) is a state-of-the-art technology for estimating the motion and position of moving objects, such as drones and robots. Tightly coupled methods have high accuracy but low real-time performanc... 展开 Visual-inertial navigation system (VINS) is a state-of-the-art technology for estimating the motion and position of moving objects, such as drones and robots. Tightly coupled methods have high accuracy but low real-time performance, while loosely coupled systems are more efficient, especially for resource-constrained systems. This study proposes a real-time loosely coupled stereo VINS based on point-line features. Unlike filter-based or optimization-based approaches, the pose estimation process is modeled as a control system. Firstly, as a loosely coupled scheme, the inertial measurement unit (IMU) data is used as the core of the proposed system. Feedback loops are introduced to correct the IMU bias, including a visual-inertial inter-frame bias estimation feedback loop and a versatile quaternion-based filter (VQF) gyroscope bias estimation feedback loop. Secondly, the accelerometer inclination correction based on VQF is introduced in the IMU propagation stage to account for slow inclination drift. To achieve better visual-inertial calibration bias results, line features are introduced in the visual part to improve accuracy and robustness. The scale of the line features is controlled by judging distance and gradient thresholds to improve the real-time performance of the edge drawing lines (EDLines) algorithm. The proposed method is evaluated on the EuRoC MAV dataset and tested on the KITTI dataset. The comparisons with previous methods on benchmark datasets demonstrate the effectiveness of the proposed method in terms of localization accuracy and real-time performance. 收起