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：在高度动态环境中，基于迭代最近点（ICP）配准的里程计仍面临挑战：ICP假设场景近似静态，在重复性或低纹理几何结构中性能会下降。本文提出Dynamic-ICP，一种多普勒感知的配准框架。该方法（i）通过鲁棒回归从逐点多普勒速度估计自车运动并构建速度滤波器，（ii）聚类动态物体并根据自车运动补偿后的径向测量重建物体级平移速度，（iii）使用恒定速度模型预测动态点，（iv）通过融合点对面几何残差与平移不变、仅含旋转分量的多普勒残差的紧凑目标函数进行扫描对齐。该方法无需外部传感器或传感器-车辆标定，可直接处理调频连续波（FMCW）激光雷达的距离与多普勒速度数据。我们在三个数据集——HeRCULES、HeLiPR、AevaScenes——上评估Dynamic-ICP，重点关注高度动态场景。实验表明，Dynamic-ICP在旋转稳定性和平移精度上均持续优于现有先进方法。本方法易于集成至现有流程，可实时运行，为动态环境中的鲁棒配准提供了轻量级解决方案。为促进进一步研究，代码已开源：https://github.com/JMUWRobotics/Dynamic-ICP。