发表机构
SNU（首尔国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RaDiVe框架，通过距离约束NDT、速度差异点不确定性模型及SDF表面点提取构建鲁棒4D雷达里程计，在多数据集上优于现有基线且保持实时性
AI 中文摘要
4D雷达的最新进展可在恶劣天气下实现鲁棒感知，但雷达点云固有的稀疏性、噪声及有限的位置精度，给基于配准的里程计带来重大挑战。本文提出RaDiVe，一种旨在提升雷达点云配准精度与鲁棒性的4D雷达里程计框架。我们引入距离约束正态分布变换（Normal Distributions Transform，NDT），通过将对应关系搜索限制在近距离体素对，提升优化稳定性与计算效率。为缓解测量歧义，我们提出速度差异点不确定性模型，根据每个输入4D雷达点的测量多普勒径向速度与估计自运动速度预测的径向速度之间的差异，对该点进行加权。此外，我们通过隐式神经映射整合基于符号距离函数（Signed Distance Function，SDF）的表面点提取，以构建几何一致且经噪声过滤的局部子地图。在多个公开数据集上的评估表明，RaDiVe在 translational绝对轨迹误差（Absolute Trajectory Error，ATE）上平均优于现有4D雷达里程计基线44.4%，在 rotational ATE上平均优于21.3%，同时保持实时性能。源代码将向机器人社区公开：this https URL
英文摘要
Recent advances in 4D radar enable robust perception in adverse weather; however, the inherent sparsity, noise, and limited positional precision of radar point clouds pose significant challenges for registration-based odometry. In this letter, we propose RaDiVe, a 4D radar odometry framework designed to improve the accuracy and robustness of radar point-cloud registration. We introduce a distance-bounded Normal Distributions Transform (NDT), which improves optimization stability and computational efficiency by restricting the correspondence search to near-distance voxel pairs. To mitigate measurement ambiguity, we propose a velocity-discrepancy point uncertainty model that weights each input 4D radar point according to the discrepancy between its measured Doppler radial velocity and the radial velocity predicted from the estimated ego-velocity. Furthermore, we incorporate Signed Distance Function (SDF)-based surface point extraction via implicit neural mapping to construct a geometrically consistent and noise-filtered local submap. Evaluations across multiple public datasets demonstrate that RaDiVe outperforms existing 4D radar odometry baselines by 44.4% in translational Absolute Trajectory Error (ATE) and 21.3% in rotational ATE on average, while maintaining real-time performance. The source code will be made publicly available to the robotics community: https://github.com/to-be-open-sourced.
Comments8 pages, 8 figures, 8 tables
