FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917004877758464 |
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| author | Potokar, Easton R. Pool, Taylor McGann, Daniel Kaess, Michael |
| author_facet | Potokar, Easton R. Pool, Taylor McGann, Daniel Kaess, Michael |
| contents | Light Detection and Ranging (LiDAR) sensors have become a de-facto sensor for many robot state estimation tasks, spurring development of many LiDAR Odometry (LO) methods in recent years. While some smoothing-based LO methods have been proposed, most require matching against multiple scans, resulting in sub-real-time performance. Due to this, most prior works estimate a single state at a time and are ``submap''-based. This architecture propagates any error in pose estimation to the fixed submap and can cause jittery trajectories and degrade future registrations. We propose Fixed-Lag Odometry with Reparative Mapping (FORM), a LO method that performs smoothing over a densely connected factor graph while utilizing a single iterative map for matching. This allows for both real-time performance and active correction of the local map as pose estimates are further refined. We evaluate on a wide variety of datasets to show that FORM is robust, accurate, real-time, and provides smooth trajectory estimates when compared to prior state-of-the-art LO methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors Potokar, Easton R. Pool, Taylor McGann, Daniel Kaess, Michael Robotics Light Detection and Ranging (LiDAR) sensors have become a de-facto sensor for many robot state estimation tasks, spurring development of many LiDAR Odometry (LO) methods in recent years. While some smoothing-based LO methods have been proposed, most require matching against multiple scans, resulting in sub-real-time performance. Due to this, most prior works estimate a single state at a time and are ``submap''-based. This architecture propagates any error in pose estimation to the fixed submap and can cause jittery trajectories and degrade future registrations. We propose Fixed-Lag Odometry with Reparative Mapping (FORM), a LO method that performs smoothing over a densely connected factor graph while utilizing a single iterative map for matching. This allows for both real-time performance and active correction of the local map as pose estimates are further refined. We evaluate on a wide variety of datasets to show that FORM is robust, accurate, real-time, and provides smooth trajectory estimates when compared to prior state-of-the-art LO methods. |
| title | FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.09966 |