IMU Propagation as Preintegration
For practitioners in visual-inertial, lidar-inertial, and radar-inertial state estimation, this work simplifies code reuse and provides consistency checks for preintegration implementations.
The paper demonstrates that IMU preintegration and propagation are equivalent, allowing preintegrated measurements to be obtained by wrapping existing propagation routines. Experiments show close agreement between RK4-based propagation and GTSAM preintegration modules in Jacobians, covariances, and transition matrices.
IMU preintegration is widely used in factor-graph-based visual--inertial, lidar--inertial, and radar--inertial state estimation, yet it is often treated as a specialized implementation separate from conventional IMU propagation. This note shows that IMU preintegration and propagation are equivalent realizations of the same underlying computation. We present a convention-agnostic view in which the preintegrated measurement, bias Jacobians, and covariance can be obtained by wrapping an existing IMU propagation routine, while a preintegration module can conversely recover state-transition matrices and propagated covariances. This perspective simplifies the reuse of existing propagation code, supports translation across different error-state definitions, and provides practical consistency checks for preintegration implementations. Experiments with random IMU sequences demonstrate close agreement between an RK4-based propagation implementation and GTSAM's tangent and manifold preintegration modules in the recovered Jacobians, covariances, and transition matrices.