用4D雷达实现复杂场景下的精准回环检测，提升机器人定位精度。
Introspective Loop Closure for SLAM with 4D Imaging Radar
- 构建子地图增强环境表征，结合自省机制过滤误检。
- 在相似与相反视角下均实现高精度回环检测，最优轨迹误差降低82%。
- 适用于视觉受阻、点云稀疏的复杂环境，适合自动驾驶等场景。
同时定位与地图构建（SLAM）使移动机器人可在无外部定位系统或预存地图的情况下导航。雷达因其对颗粒物干扰不敏感，正成为重要传感工具，尤其在视觉受阻环境中表现优异。现代4D成像雷达可提供三维几何信息和相对速度测量，但存在视场小、点云稀疏且噪声大等问题。在SLAM中检测回环对于减少轨迹漂移、保持地图准确性至关重要。然而，4D雷达数据的方向性使得从相反视角识别回环困难，因扫描重叠度低。本文研究使用4D雷达进行SLAM中的回环检测，重点关注相似与相反视角。通过生成子地图以获得更密集的环境表示，并利用自省度量在特征退化环境中排除误检。实验表明，在几何多样的设置下，对相似与相反视角均能实现准确的回环检测，使轨迹估计精度提升最高达82%，并在自相似环境中有效拒绝虚假阳性结果。
原文摘要 · Abstract (English)
Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is less affected by particles than lidars or cameras. Modern 4D imaging radars provide three-dimensional geometric information and relative velocity measurements, but they bring challenges, such as a small field of view and sparse, noisy point clouds. Detecting loop closures in SLAM is critical for reducing trajectory drift and maintaining map accuracy. However, the directional nature of 4D radar data makes identifying loop closures, especially from reverse viewpoints, difficult due to limited scan overlap. This article explores using 4D radar for loop closure in SLAM, focusing on similar and opposing viewpoints. We generate submaps for a denser environment representation and use introspective measures to reject false detections in feature-degenerate environments. Our experiments show accurate loop closure detection in geometrically diverse settings for both similar and opposing viewpoints, improving trajectory estimation with up to 82 % improvement in ATE and rejecting false positives in self-similar environments.
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