对比13种雷达点云提取方法，发现最简单快速的K-strongest表现最佳。
The Finer Points: A Systematic Comparison of Point-Cloud Extractors for Radar Odometry
- 系统比较13种点云提取算法在雷达里程计中的表现。
- K-strongest在两个数据集上分别领先平均性能13.59%和24.94%。
- 参数调优显著提升精度，适合自动驾驶雷达系统优化者。
基于旋转式调频连续波（FMCW）雷达的许多里程计流程中，从原始信号中提取点云是关键步骤，对基于点云的里程计性能有重大影响。本文首次系统性地对比了13种常见的雷达点云提取器在自主驾驶环境下的迭代最近点（ICP）里程计任务中的表现。所有提取器的参数均在两个FMCW雷达数据集上进行调优与测试，覆盖约176公里公共道路数据。结果表明，最简单且最快的K-strongest提取器表现最优，在两个数据集上分别优于平均性能13.59%和24.94%。此外，本文强调了参数调优的重要性，其可带来显著的里程计精度提升。
原文摘要 · Abstract (English)
A key element of many odometry pipelines using spinning frequency-modulated continuous-wave (FMCW) radar is the extraction of a point-cloud from the raw signal. This extraction greatly impacts the overall performance of point-cloud-based odometry. This paper provides a first-of-its-kind, comprehensive comparison of 13 common radar point-cloud extractors for the task of iterative closest point based odometry in autonomous driving environments. Each extractor's parameters are tuned and tested on two FMCW radar datasets using approximately 176km of data from public roads. We find that the simplest, and fastest extractor, K-strongest, is the best overall extractor, consistently outperforming the average by 13.59% and 24.94% on each dataset, respectively. Additionally, we highlight the significance of tuning an extractor and the substantial improvement in odometry accuracy that it yields.
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