4D millimeter-wave (mmWave) radars are sensors that provide robustness against adverse weather conditions (rain, snow, fog, etc.), and as such they are increasingly used for odometry and SLAM (Simultaneous Location and Mapping). However, the noisy and sparse nature of the returned scan data proves to be a challenging obstacle for existing registration algorithms, especially those originally intended for more accurate sensors such as LiDAR. Following the success of 3D Gaussian Splatting for vision, in this paper we propose a summarized representation for radar scenes based on global simultaneous optimization of 3D Gaussians as opposed to voxel-based approaches, and leveraging its inherent Probability Density Function (PDF) for registration. Moreover, we propose optimizing multiple registration hypotheses for better protection against local optima of the PDF. We evaluate our modeling and registration system against state of the art techniques, finding that our system provides richer models and more accurate registration results. Finally, we evaluate the effectiveness of our system in a real Radar-Inertial Odometry task. Experiments using publicly available 4D radar datasets show that our Gaussian approach is comparable to existing registration algorithms, outperforming them in several sequences. Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

翻译：4D毫米波雷达是一种能够在恶劣天气条件（雨、雪、雾等）下保持鲁棒性的传感器，因此正日益广泛地应用于里程计与同步定位与地图构建任务中。然而，雷达扫描数据固有的噪声与稀疏特性，对现有的配准算法构成了严峻挑战，尤其是那些最初为激光雷达等更精确传感器设计的算法。受视觉领域中3D高斯泼溅技术成功的启发，本文提出了一种基于雷达场景的概括性表征方法。该方法通过对3D高斯分布进行全局同步优化来构建场景模型，而非采用基于体素的方法，并利用其固有的概率密度函数进行配准。此外，我们提出优化多个配准假设，以更好地避免陷入概率密度函数的局部最优解。我们将所提出的建模与配准系统与现有先进技术进行了对比评估，结果表明我们的系统能够提供更丰富的模型和更精确的配准结果。最后，我们在真实的雷达-惯性里程计任务中评估了该系统的有效性。基于公开可用的4D雷达数据集进行的实验表明，我们的高斯方法在性能上与现有配准算法相当，并在多个数据序列中表现更优。版权所有 2026 IEEE。允许个人使用本材料。所有其他用途，包括在任何当前或未来的媒体中为广告或促销目的重印/再版本材料、创建新的集体作品、转售或重新分发至服务器或列表、或在其他作品中重用本作品的任何受版权保护部分，均须获得IEEE的许可。