{"ID":2856957,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09966","arxiv_id":"2510.09966","title":"FORM: Fixed-Lag Odometry with Reparative Mapping utilizing Rotating LiDAR Sensors","abstract":"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.","short_abstract":"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 pe...","url_abs":"https://arxiv.org/abs/2510.09966","url_pdf":"https://arxiv.org/pdf/2510.09966v1","authors":"[\"Easton R. Potokar\",\"Taylor Pool\",\"Daniel McGann\",\"Michael Kaess\"]","published":"2025-10-11T02:40:25Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}