用雷达实现激光雷达级定位？CFEAR方法在多样环境中实现精确、快速且鲁棒的大规模雷达里程计
Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
AI总结：
本文提出CFEAR雷达里程计扩展方法，通过运动补偿、一对多配准和新求解策略，在多样环境中无需调参即可实现高精度快速定位，性能超越雷达SLAM并接近激光雷达SLAM。
摘要：
本文提出了一种精确、高效且无需学习的大规模里程计估计方法，使用旋转雷达，经验表明该方法在非常多样的环境中——从城市到林地的室外环境，以及仓库和矿井的室内环境——无需改变参数即可良好泛化。我们的方法将单次扫描内的运动补偿与一对多扫描配准相结合，最小化邻近有向表面点之间的距离，并用鲁棒损失函数抑制离群点。在扩展我们先前方法CFEAR的基础上，我们对更广泛的数据集进行了深入研究，量化了滤波、分辨率、配准代价与损失函数、关键帧历史以及运动补偿的重要性。我们提出了一种新的求解策略和配置，克服了先前在稀疏性和偏差方面的问题，并将我们最先进的方法性能提升了38%，从而出人意料地超越了雷达SLAM，并接近激光雷达SLAM。最精确的配置在牛津基准上以5Hz实现了1.09%的误差，最快的配置在160Hz下实现了1.79%的误差。
英文摘要：
This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.
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。
