4D iRIOM：4D成像雷达惯性里程计与建图
4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping
AI总结：
本文提出首个4D雷达惯性SLAM系统iRIOM，通过渐进非凸方法估计自速度、分布到多分布距离匹配及迭代扩展卡尔曼滤波融合，实现高精度实时里程计与建图。
摘要：
毫米波雷达能够在雾等恶劣条件下测量物体的距离、方向和多普勒速度。具有类似图像的垂直和水平数据的4D成像雷达还可以测量物体的高度。先前的研究使用3D雷达进行自运动估计。但很少有方法利用成像雷达的丰富数据，并且它们通常忽略了建图方面，从而导致里程计精度较差。本文提出了一种基于子图概念的实时成像雷达惯性里程计与建图方法iRIOM。为了处理移动物体和多径反射，我们使用渐进非凸方法从单次扫描中鲁棒且高效地估计自速度。为了测量稀疏非重复雷达扫描点与子图点之间的一致性，采用了分布到多分布距离进行匹配。自速度和扫描到子图的匹配通过迭代扩展卡尔曼滤波器与6D惯性数据融合，以获取平台的3D位置和方向。还开发了回环闭合模块以抑制里程计模块的漂移。据我们所知，基于这两个模块的iRIOM是首个4D雷达惯性SLAM系统。在我们的和第三方数据上，我们展示了iRIOM相对于FastLIO-SLAM和EKFRIO的优越里程计精度和建图一致性。此外，消融研究揭示了惯性数据相对于恒速模型的优势，以及扫描到子图匹配相对于扫描到扫描匹配的优势。
英文摘要：
Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This paper presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching.
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