用激光雷达标签训练雷达语义分割，实现无需人工标注的高精度分割。
Get It For Free: Radar Segmentation without Expert Labels and Its Application in Odometry and Localization
- 利用激光雷达模型生成标签，弱监督训练雷达语义分割
- 在雨雪雾条件下分割更稳定，定位误差降低20.55%
- 适用于自动驾驶中的定位与里程计，获ICRA雷达竞赛第一名
本文提出一种新的弱监督雷达语义分割方法，利用现有激光雷达语义分割模型生成语义标签，作为监督信号训练雷达语义分割模型。该模型在全天气条件下表现优于激光雷达基线模型，尤其在雪、雨、雾中更具鲁棒性。为减少激光雷达标签可能存在的错误，设计了基于结构特征与分布模式的标签修正方案。所生成的语义信息应用于两个下游任务：在基于OpenStreetMap的大规模雷达定位中，相比先前方法定位误差降低20.55%；在里程计任务中，平移精度提升16.4%，超越第二名，在日本ICRA 2024雷达机器人研讨会的雷达里程计竞赛中获得第一名。
原文摘要 · Abstract (English)
This paper presents a novel weakly supervised semantic segmentation method for radar segmentation, where the existing LiDAR semantic segmentation models are employed to generate semantic labels, which then serve as supervision signals for training a radar semantic segmentation model. The obtained radar semantic segmentation model outperforms LiDAR-based models, providing more consistent and robust segmentation under all-weather conditions, particularly in the snow, rain and fog. To mitigate potential errors in LiDAR semantic labels, we design a dedicated refinement scheme that corrects erroneous labels based on structural features and distribution patterns. The semantic information generated by our radar segmentation model is used in two downstream tasks, achieving significant performance improvements. In large-scale radar-based localization using OpenStreetMap, it leads to localization error reduction by 20.55\% over prior methods. For the odometry task, it improves translation accuracy by 16.4\% compared to the second-best method, securing the first place in the radar odometry competition at the Radar in Robotics workshop of ICRA 2024, Japan
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