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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.23359 |
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| _version_ | 1866912672520339456 |
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| author | Tian, Chungeng Hao, Ning He, Fenghua |
| author_facet | Tian, Chungeng Hao, Ning He, Fenghua |
| contents | This paper presents a novel approach to address the inconsistency problem caused by observability mismatch in visual-inertial navigation systems (VINS). The key idea involves applying a linear time-varying transformation to the error-state within the Error-State Kalman Filter (ESKF). This transformation ensures that \textrr{the unobservable subspace of the transformed error-state system} becomes independent of the state, thereby preserving the correct observability of the transformed system against variations in linearization points. We introduce the Transformed ESKF (T-ESKF), a consistent VINS estimator that performs state estimation using the transformed error-state system. Furthermore, we develop an efficient propagation technique to accelerate the covariance propagation based on the transformation relationship between the transition and accumulated matrices of T-ESKF and ESKF. We validate the proposed method through extensive simulations and experiments, demonstrating better (or competitive at least) performance compared to state-of-the-art methods. The code is available at github.com/HITCSC/T-ESKF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation Tian, Chungeng Hao, Ning He, Fenghua Robotics This paper presents a novel approach to address the inconsistency problem caused by observability mismatch in visual-inertial navigation systems (VINS). The key idea involves applying a linear time-varying transformation to the error-state within the Error-State Kalman Filter (ESKF). This transformation ensures that \textrr{the unobservable subspace of the transformed error-state system} becomes independent of the state, thereby preserving the correct observability of the transformed system against variations in linearization points. We introduce the Transformed ESKF (T-ESKF), a consistent VINS estimator that performs state estimation using the transformed error-state system. Furthermore, we develop an efficient propagation technique to accelerate the covariance propagation based on the transformation relationship between the transition and accumulated matrices of T-ESKF and ESKF. We validate the proposed method through extensive simulations and experiments, demonstrating better (or competitive at least) performance compared to state-of-the-art methods. The code is available at github.com/HITCSC/T-ESKF. |
| title | T-ESKF: Transformed Error-State Kalman Filter for Consistent Visual-Inertial Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.23359 |