Open_vins is a visual-inertial odometry framework and SLAM system designed for robotic state estimation. It uses an Extended Kalman Filter to fuse high-frequency inertial sensor data with visual feature tracks to estimate the position and orientation of a moving device.
The system features a sensor calibration suite for calculating intrinsic and extrinsic parameters, as well as temporal offsets between cameras and inertial measurement units. It includes a manifold interpolator that uses B-Spline curves over the special Euclidean group to produce smooth trajectory paths between discrete pose estimates.
The project covers visual processing for feature extraction, tracking, and triangulation, utilizing multiple camera models to correct lens distortion. It implements inertial preintegration to reduce computational load, supports both stationary and dynamic state initialization, and employs a secondary-thread loop closure process to correct global drift.
Additional utilities provide multi-sensor data simulation, trajectory performance evaluation, and tools for exporting map optimization data.