arXiv 2405.097802025-06-26cs.RO

EFEAR-4D：基于自车速度过滤的高效高精度4D雷达里程计

EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry

Department of Automation, School of Mechanical Engineering and Automation, Harbin Institute of Technology（自动化系，机械工程与自动化学院，哈尔滨工业大学）；Research Institute of Intelligent Control and Systems, Harbin Institute of Technology（智能控制与系统研究院，哈尔滨工业大学）

Xiaoyi Wu, Yushuai Chen, Zhan Li, Ziyang Hong, Liang Hu

DOI：: 10.1109/LRA.2024.3466071

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AI总结：

本文提出EFEAR-4D，一种无需学习的4D雷达里程计方法，利用多普勒速度进行鲁棒的自车速度估计，并通过动态物体移除和区域特征提取应对点云稀疏性与噪声，在公开数据集上实现了最先进的定位精度和可靠性。

摘要：

里程计是成功实现自主导航的关键组件，依赖于相机、LiDAR和IMU等传感器。然而，这些传感器在降雪和浓雾等极端天气条件下可能面临挑战。FMCW雷达技术的出现为在恶劣条件下实现鲁棒感知提供了可能。作为最新一代的FMCW雷达，4D毫米波雷达提供包含距离、方位角、俯仰角和多普勒速度信息的点云，尽管点云中存在固有的稀疏性和噪声。在本文中，我们提出了EFEAR-4D，一种用于大规模4D雷达里程计估计的准确、高效且无需学习的方法。EFEAR-4D巧妙地利用多普勒速度信息进行鲁棒的自车速度估计，从而得到高精度的先验猜测。EFEAR-4D通过动态物体移除和有效的区域特征提取，在多样环境中保持对点云稀疏性和噪声的鲁棒性。在两个公开可用的4D雷达数据集上的大量实验表明，EFEAR-4D在各种条件下具有最先进的可靠性和定位精度。此外，我们收集了一个数据集，遵循相同路线但改变4D雷达的安装高度，强调了雷达高度对点云质量的显著影响——这是现实世界部署的关键考虑因素。我们的算法和数据集将很快在https://github.com/CLASS-Lab/EFEAR-4D提供。

英文摘要：

Odometry is a crucial component for successfully implementing autonomous navigation, relying on sensors such as cameras, LiDARs and IMUs. However, these sensors may encounter challenges in extreme weather conditions, such as snowfall and fog. The emergence of FMCW radar technology offers the potential for robust perception in adverse conditions. As the latest generation of FWCW radars, the 4D mmWave radar provides point cloud with range, azimuth, elevation, and Doppler velocity information, despite inherent sparsity and noises in the point cloud. In this paper, we propose EFEAR-4D, an accurate, highly efficient, and learning-free method for large-scale 4D radar odometry estimation. EFEAR-4D exploits Doppler velocity information delicately for robust ego-velocity estimation, resulting in a highly accurate prior guess. EFEAR-4D maintains robustness against point-cloud sparsity and noises across diverse environments through dynamic object removal and effective region-wise feature extraction. Extensive experiments on two publicly available 4D radar datasets demonstrate state-of-the-art reliability and localization accuracy of EFEAR-4D under various conditions. Furthermore, we have collected a dataset following the same route but varying installation heights of the 4D radar, emphasizing the significant impact of radar height on point cloud quality - a crucial consideration for real-world deployments. Our algorithm and dataset will be available soon at https://github.com/CLASS-Lab/EFEAR-4D.

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