An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908916631207936 |
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| author | Cerezo, Samuel Lee, Seong Hun Civera, Javier |
| author_facet | Cerezo, Samuel Lee, Seong Hun Civera, Javier |
| contents | In this letter, we present a closed-form initialization method that recovers the full visual-inertial state without nonlinear optimization. Unlike previous approaches that rely on iterative solvers, our formulation yields analytical, easy-to-implement, and numerically stable solutions for reliable start-up. Our method builds on small-rotation and constant-velocity approximations, which keep the formulation compact while preserving the essential coupling between motion and inertial measurements. We further propose an observability-driven, two-stage initialization scheme that balances accuracy with initialization latency. Extensive experiments on the EuRoC dataset validate our assumptions: our method achieves 10-20% lower initialization error than optimization-based approaches, while using 4x shorter initialization windows and reducing computational cost by 5x. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization Cerezo, Samuel Lee, Seong Hun Civera, Javier Robotics In this letter, we present a closed-form initialization method that recovers the full visual-inertial state without nonlinear optimization. Unlike previous approaches that rely on iterative solvers, our formulation yields analytical, easy-to-implement, and numerically stable solutions for reliable start-up. Our method builds on small-rotation and constant-velocity approximations, which keep the formulation compact while preserving the essential coupling between motion and inertial measurements. We further propose an observability-driven, two-stage initialization scheme that balances accuracy with initialization latency. Extensive experiments on the EuRoC dataset validate our assumptions: our method achieves 10-20% lower initialization error than optimization-based approaches, while using 4x shorter initialization windows and reducing computational cost by 5x. |
| title | An Efficient Closed-Form Solution to Full Visual-Inertial State Initialization |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.18910 |