{"ID":2836957,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20292","arxiv_id":"2511.20292","title":"Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes","abstract":"Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego motion from per-point Doppler velocity via robust regression and builds a velocity filter, (ii) clusters dynamic objects and reconstructs object-wise translational velocities from ego-compensated radial measurements, (iii) predicts dynamic points with a constant-velocity model, and (iv) aligns scans using a compact objective that combines point-to-plane geometry residual with a translation-invariant, rotation-only Doppler residual. The approach requires no external sensors or sensor-vehicle calibration and operates directly on FMCW LiDAR range and Doppler velocities. We evaluate Dynamic-ICP on three datasets-HeRCULES, HeLiPR, AevaScenes-focusing on highly dynamic scenes. Dynamic-ICP consistently improves rotational stability and translation accuracy over the state-of-the-art methods. Our approach is also simple to integrate into existing pipelines, runs in real time, and provides a lightweight solution for robust registration in dynamic environments. To encourage further research, the code is available at: https://github.com/JMUWRobotics/Dynamic-ICP.","short_abstract":"Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego motion from per-point Doppler v...","url_abs":"https://arxiv.org/abs/2511.20292","url_pdf":"https://arxiv.org/pdf/2511.20292v3","authors":"[\"Dong Wang\",\"Daniel Casado Herraez\",\"Stefan May\",\"Andreas Nüchter\"]","published":"2025-11-25T13:21:27Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false,"code_links":[{"ID":606644,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2836957,"paper_url":"https://arxiv.org/abs/2511.20292","paper_title":"Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes","repo_url":"https://github.com/JMUWRobotics/Dynamic-ICP","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}