重新定义雷达分割：利用雷达点云实现同时静态-移动分割和自主运动估计
Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds
AI总结：
本研究提出一种基于神经网络的方法，利用雷达点云同时实现静态-移动分割和自主运动估计，无需中间信号处理步骤。
摘要：
传统雷达分割研究通常集中在学习不同移动物体的类别标签上。尽管雷达和光学传感器之间存在根本差异，导致预测准确且一致的类别标签的可靠性不同，但对汽车中常见雷达感知任务的回顾表明，确定一个物体是移动还是静态是大多数任务的前提。为填补这一空白，本研究提出了一种基于神经网络的解决方案，能够同时从雷达点云中分割静态和移动物体。此外，由于静态物体的测量径向速度与雷达的运动相关，这种方法还能估计移动平台或车辆（自主运动）的瞬时2D速度。然而，尽管执行双重任务，所提出的方法使用了非常简单但有效的特征提取构建块：多层感知机（MLPs）和循环神经网络（RNNs）。除了在文献中首次提出外，该方法还展示了从未经处理的点云中直接提取双重任务所需信息的可行性，而无需云聚合、多普勒补偿、运动补偿或其他中间信号处理步骤。为了衡量其性能，本研究引入了一套新的评估指标，并使用具有挑战性的现实世界雷达数据集RadarScenes测试了所提出的方法。结果表明，所提出的方法不仅在双重任务上表现良好，而且在其他雷达感知任务中具有广阔的应用前景。
英文摘要：
Conventional radar segmentation research has typically focused on learning category labels for different moving objects. Although fundamental differences between radar and optical sensors lead to differences in the reliability of predicting accurate and consistent category labels, a review of common radar perception tasks in automotive reveals that determining whether an object is moving or static is a prerequisite for most tasks. To fill this gap, this study proposes a neural network based solution that can simultaneously segment static and moving objects from radar point clouds. Furthermore, since the measured radial velocity of static objects is correlated with the motion of the radar, this approach can also estimate the instantaneous 2D velocity of the moving platform or vehicle (ego motion). However, despite performing dual tasks, the proposed method employs very simple yet effective building blocks for feature extraction: multi layer perceptrons (MLPs) and recurrent neural networks (RNNs). In addition to being the first of its kind in the literature, the proposed method also demonstrates the feasibility of extracting the information required for the dual task directly from unprocessed point clouds, without the need for cloud aggregation, Doppler compensation, motion compensation, or any other intermediate signal processing steps. To measure its performance, this study introduces a set of novel evaluation metrics and tests the proposed method using a challenging real world radar dataset, RadarScenes. The results show that the proposed method not only performs well on the dual tasks, but also has broad application potential in other radar perception tasks.
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。
