基于4D成像雷达的SLAM内省式回环闭合
Introspective Loop Closure for SLAM with 4D Imaging Radar
AI总结：
本文探讨使用4D成像雷达在SLAM中进行回环闭合，通过生成子地图和内省措施在特征退化环境中拒绝误检，实现相似与相反视角的准确检测，显著改善轨迹估计。
摘要：
同步定位与建图（SLAM）使移动机器人能够在没有外部定位系统或预先存在地图的情况下进行导航。雷达正成为一种有价值的传感工具，特别是在视觉受阻的环境中，因为它比激光雷达或相机更少受颗粒影响。现代4D成像雷达提供三维几何信息和相对速度测量，但它们带来了挑战，例如视场小和点云稀疏且有噪声。在SLAM中检测回环闭合对于减少轨迹漂移和维持地图精度至关重要。然而，4D雷达数据的方向性使得识别回环闭合变得困难，特别是由于扫描重叠有限而难以从相反视角识别。本文探讨了在SLAM中使用4D雷达进行回环闭合，关注相似和相反视角。我们生成子地图以获得更密集的环境表示，并使用内省措施在特征退化环境中拒绝错误检测。我们的实验表明，在几何多样的环境中，对于相似和相反视角都能准确检测回环闭合，将ATE提高了最多82%，并在自相似环境中拒绝误报。
英文摘要：
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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