基于高斯建模和多假设扫描匹配的4D雷达-惯性里程计
4D Radar-Inertial Odometry based on Gaussian Modeling and Multi-Hypothesis Scan Matching
AI总结：
本文提出基于高斯分布的4D雷达场景表示方法，通过全局优化提升注册精度，并通过多假设优化避免局部最优，实验表明其在雷达-惯性里程计任务中表现优异。
摘要：
4D毫米波雷达在恶劣天气下具有鲁棒性，但其噪声和稀疏的扫描数据对现有注册算法构成挑战。本文提出基于3D高斯分布的全局优化方法，利用其概率密度函数进行注册，并通过多假设优化提升鲁棒性。实验表明，该方法在4D雷达-惯性里程计任务中表现优异，与现有算法相比在多个序列中表现更佳。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
