Abstract
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 this http URL.
Abstract (translated)
用于环境感知的雷达传感器,例如在自主车辆中使用时,通常会输出许多不必要的散射项。这些点没有对应的实际对象存在,是进行对象检测或跟踪等后续处理步骤的主要错误来源。因此,我们提出了两个新的神经网络架构,用于识别散射项。输入数据、网络结构和训练配置专门针对这一任务进行了调整。特别关注了由多个传感器扫描组成的点云的采样。在广泛的评估中,新的架构表现出比现有方法更好的性能。因为缺少包含散射项注释的合适公开数据集,我们设计了一种方法,可以自动生成相应的标签。将该方法应用于包含对象注释的现有数据并释放其代码,我们有效地创造了第一个免费的雷达散射项数据集,代表现实世界驾驶场景。代码和指令可在本网站上访问。
URL
https://arxiv.org/abs/2303.09530
