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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2605.18047 |
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| _version_ | 1866914586812219392 |
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| author | Wu, Wei Chen, Honglin Cao, Wenhan Lyu, Yao Xu, Shaobing Jiang, Kun Li, Jiangtao Zhang, Tao Guo, Lei Li, Shengbo Eben |
| author_facet | Wu, Wei Chen, Honglin Cao, Wenhan Lyu, Yao Xu, Shaobing Jiang, Kun Li, Jiangtao Zhang, Tao Guo, Lei Li, Shengbo Eben |
| contents | Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-specific designs. Such binding makes it difficult to vary one design choice without re-engineering the rest of the state-estimation process. This paper presents FUSE, a framework for unified state estimation in vehicular and robotic SLAM systems. FUSE organizes the state-estimation interface around observation ingestion, propagation, update, and state query, and uses this interface to separate temporal processing, residual-ready local geometric association, estimator formulation, and map-update policy. A LiDAR--IMU instantiation is developed to examine the framework under mixed-rate sensing and directional degeneracy, where high-rate inertial propagation, LiDAR-triggered geometric update, residual screening, and degeneracy-aware correction operate through the same interface boundaries. On a 418~m loop-corridor sequence, the instantiation reports a 1.626 m end-to-end trajectory error, corresponding to a 7.9% relative error reduction compared with Faster-LIO, the lowest-error baseline on this sequence. The results support FUSE as a framework for organizing state-estimation design choices and show how the evaluated instantiation regularizes updates along weakly observable directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_18047 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems Wu, Wei Chen, Honglin Cao, Wenhan Lyu, Yao Xu, Shaobing Jiang, Kun Li, Jiangtao Zhang, Tao Guo, Lei Li, Shengbo Eben Robotics Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-update policy into method-specific designs. Such binding makes it difficult to vary one design choice without re-engineering the rest of the state-estimation process. This paper presents FUSE, a framework for unified state estimation in vehicular and robotic SLAM systems. FUSE organizes the state-estimation interface around observation ingestion, propagation, update, and state query, and uses this interface to separate temporal processing, residual-ready local geometric association, estimator formulation, and map-update policy. A LiDAR--IMU instantiation is developed to examine the framework under mixed-rate sensing and directional degeneracy, where high-rate inertial propagation, LiDAR-triggered geometric update, residual screening, and degeneracy-aware correction operate through the same interface boundaries. On a 418~m loop-corridor sequence, the instantiation reports a 1.626 m end-to-end trajectory error, corresponding to a 7.9% relative error reduction compared with Faster-LIO, the lowest-error baseline on this sequence. The results support FUSE as a framework for organizing state-estimation design choices and show how the evaluated instantiation regularizes updates along weakly observable directions. |
| title | FUSE: A Framework for Unified State Estimation in Vehicular and Robotic SLAM Systems |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.18047 |