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 tackling the problem of radar noise entirely within the scan matching process by optimizing multiple registration hypotheses for better protection against local optima of the PDF. Finally, following existing practice we implement an Extended Kalman Filter-based Radar-Inertial Odometry pipeline in order to evaluate the effectiveness of our system. Experiments using publicly available 4D radar datasets show that our Gaussian approach is comparable to existing registration algorithms, outperforming them in several sequences.

翻译：四维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，因此越来越多地被应用于里程计与SLAM（同步定位与建图）任务中。然而，雷达返回的扫描数据具有噪声大、稀疏性强的特点，这对现有的配准算法构成了严峻挑战，尤其是那些原本为更精确的传感器（如激光雷达）设计的算法。受三维高斯溅射在视觉领域成功的启发，本文提出了一种基于雷达场景的概括性表征方法：与基于体素的方法不同，我们通过全局同步优化三维高斯模型来实现场景表征，并利用其固有的概率密度函数进行配准。此外，我们提出将雷达噪声问题完全纳入扫描匹配过程中解决，通过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。最后，遵循现有实践，我们实现了一个基于扩展卡尔曼滤波的雷达-惯性里程计流程，以评估本系统的有效性。使用公开四维雷达数据集的实验表明，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。