Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input data, network architectures and training configuration are adjusted specifically for this task. Special attention is paid to the downsampling of point clouds composed of multiple sensor scans. In an extensive evaluation, the new setups display substantially better performance than existing approaches. Because there is no suitable public data set in which clutter is annotated, we design a method to automatically generate the respective labels. By applying it to existing data with object annotations and releasing its code, we effectively create the first freely available radar clutter data set representing real-world driving scenarios. Code and instructions are accessible at www.github.com/kopp-j/clutter-ds.

翻译：用于环境感知的雷达传感器（例如自动驾驶车辆中）会产生大量不必要的杂波。这些没有对应真实物体的点云是后续处理步骤（如目标检测或跟踪）中误差的主要来源。为此，我们提出了两种新型神经网络架构来识别杂波。输入数据、网络架构和训练配置均针对此任务进行了专门调整，特别关注由多次传感器扫描组成的点云下采样过程。通过广泛评估，新方法展现出显著优于现有方案的表现。由于缺乏标注杂波的公开数据集，我们设计了一种自动化标签生成方法。通过将此方法应用于带有物体标注的现有数据并开源代码，我们有效创建了首个代表真实驾驶场景的免费雷达杂波数据集。代码与使用说明可通过 www.github.com/kopp-j/clutter-ds 获取。