Abstract
Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates the use of 4D imaging radars for SLAM and analyzes the challenges in robust loop closure. Previous work indicates that 4D radars, together with inertial measurements, offer ample information for accurate odometry estimation. However, the low field of view, limited resolution, and sparse and noisy measurements render loop closure a significantly more challenging problem. Our work builds on the previous work - TBV SLAM - which was proposed for robust loop closure with 360$^\circ$ spinning radars. This article highlights and addresses challenges inherited from a directional 4D radar, such as sparsity, noise, and reduced field of view, and discusses why the common definition of a loop closure is unsuitable. By combining multiple quality measures for accurate loop closure detection adapted to 4D radar data, significant results in trajectory estimation are achieved; the absolute trajectory error is as low as 0.46 m over a distance of 1.8 km, with consistent operation over multiple environments.
Abstract (translated)
成像雷达是在定位与导航（SLAM）场景中新兴的传感器模块，特别是适用于视觉受限的环境。本文研究了使用4D成像雷达进行SLAM，并分析了稳健环闭合的挑战。之前的工作表明，4D雷达与惯性测量相结合可以提供足够的信息来进行精确的测距估计。然而，低视场、有限分辨率以及稀疏和嘈杂的测量使环闭合变得更具挑战性。本文的工作是在之前的工作基础上 - TBV SLAM - 提出的，该工作提出了使用360$^\circ$旋转雷达进行稳健环闭合。本文突出了并解决了从方向性4D雷达中继承的挑战，如稀疏性、噪声和视场限制，并讨论了为什么常见的环闭合定义不适合。通过将多种4D雷达数据的质量度量适应该功能的环闭合检测，获得了轨迹估计的显著结果；绝对轨迹误差在1.8公里距离下为0.46米，在多个环境下具有稳定的操作。
URL
https://arxiv.org/abs/2404.03940
