迈向4D雷达SLAM中的内省式回环检测
Towards introspective loop closure in 4D radar SLAM
AI总结：
本文针对4D成像雷达SLAM中因视场小、分辨率低和测量稀疏噪声导致的回环检测难题，在TBV SLAM基础上提出结合多种适配质量度量的方法，实现了1.8公里距离上0.46米的绝对轨迹误差。
摘要：
成像雷达是定位与建图（SLAM）背景下一种新兴的传感器模态，尤其适用于视觉受阻的环境。本文研究了4D成像雷达在SLAM中的应用，并分析了鲁棒回环检测所面临的挑战。先前的工作表明，4D雷达结合惯性测量能够为精确的里程计估计提供充分的信息。然而，较低的视场角、有限的分辨率以及稀疏且带噪声的测量使得回环检测成为一个显著更具挑战性的问题。我们的工作建立在先前的工作——TBV SLAM——之上，该工作是为使用360°旋转雷达实现鲁棒回环检测而提出的。本文着重指出并解决了由定向4D雷达继承而来的挑战，例如稀疏性、噪声和减小的视场角，并讨论了常见的回环定义为何不适用。通过结合多种针对4D雷达数据适配的质量度量来实现精确的回环检测，在轨迹估计方面取得了显著成果；在1.8公里的距离上绝对轨迹误差低至0.46米，并且在多种环境下均能稳定运行。
英文摘要：
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.
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。
