4D millimeter-wave (mmWave) radars are sensors that provide robustness against adverse weather conditions (rain, snow, fog, etc.), and as such they are increasingly being used for odometry and SLAM applications. However, the noisy and sparse nature of the returned scan data proves to be a challenging obstacle for existing point cloud matching based solutions, especially those originally intended for more accurate sensors such as LiDAR. Inspired by visual odometry research around 3D Gaussian Splatting, in this paper we propose using freely positioned 3D Gaussians to create a summarized representation of a radar point cloud tolerant to sensor noise, and subsequently leverage its inherent probability distribution function for registration (similar to NDT). Moreover, we propose simultaneously optimizing multiple scan matching hypotheses in order to further increase the robustness of the system against local optima of the function. Finally, we fuse our Gaussian modeling and scan matching algorithms into an EKF radar-inertial odometry system designed after current best practices. Experiments show that our Gaussian-based odometry is able to outperform current baselines on a well-known 4D radar dataset used for evaluation.
4D 毫米波 (mmWave) 雷达因其在恶劣天气条件（如雨、雪、雾等）下的鲁棒性，正越来越多地被应用于里程计和 SLAM 系统。然而，由于雷达返回的扫描数据通常具有噪声和稀疏的特性，现有基于点云匹配的解决方案（尤其是那些针对更高精度传感器如 LiDAR 设计的方法）面临较大挑战。 受基于三维高斯点云（3D Gaussian Splatting）的视觉里程计研究的启发，本文提出了一种利用自由定位的三维高斯来生成雷达点云的摘要表示的方法。该表示对传感器噪声具有较高容忍度，并利用其固有的概率分布函数进行配准（类似于 NDT 方法）。此外，我们提出了多配准假设的同时优化，以进一步提高系统在面对函数局部最优时的鲁棒性。 最终，我们将高斯建模和扫描匹配算法整合到一个基于扩展卡尔曼滤波（EKF）的雷达-惯性里程计系统中，设计遵循当前最佳实践。实验表明，我们基于高斯的里程计在一个知名的 4D 雷达数据集上的性能优于现有基线方法，展现了其在雷达点云处理中的强大潜力。