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.

翻译：摘要：在强动态环境下，基于ICP（迭代最近点）配准的稳健里程计仍具挑战性：ICP假设场景近乎静态，且在重复或低纹理几何结构中性能下降。本文提出Dynamic-ICP——一种多普勒感知配准框架。该方法通过以下步骤实现：（i）根据逐点多普勒速度进行稳健回归估计自运动，并构建速度滤波器；（ii）聚类动态物体，并从自运动补偿后的径向测量中重建物体平动速度；（iii）采用恒定速度模型预测动态点；（iv）利用紧凑目标函数对齐扫描点云，该函数结合了点面几何残差与平移不变、仅含旋转的多普勒残差。本方法无需外部传感器或传感器-车辆标定，可直接基于FMCW激光雷达的测距和多普勒速度进行运算。我们在三个数据集（HeRCULES、HeLiPR、AevaScenes）上聚焦强动态场景对Dynamic-ICP进行评估。与现有最优方法相比，Dynamic-ICP在旋转稳定性与平移精度方面均实现持续改进。本方法易于集成至现有管线，可实时运行，并为动态环境中的稳健配准提供轻量化解决方案。为促进后续研究，代码已开源至：https://github.com/JMUWRobotics/Dynamic-ICP。