IMU Propagation as Preintegration
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911724374851584 |
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| author | Huai, Jianzhu |
| author_facet | Huai, Jianzhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IMU Propagation as Preintegration Huai, Jianzhu Robotics 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. |
| title | IMU Propagation as Preintegration |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.28279 |