论文标题
扩展雷达跟踪的基于RLS的瞬时速度估计器
An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking
论文作者
论文摘要
雷达传感器已成为感知传感器套件的重要组成部分，因为它们的远距离和在不利天气条件下工作的能力。但是，几个缺点，例如大量的噪音和点云的极端稀疏性导致它们没有充分利用其潜力。在本文中，我们提出了一种基于新颖的递归最小二乘（RLS）方法，以实时估计动态对象的瞬时速度，能够处理输入数据流中的大量噪声。我们还提出了一条端到端管道，以实时跟踪扩展对象，该对象使用数据关联和跟踪初始化的计算速度估计值。使用几种测试这些算法限制的现实世界启发的驾驶场景来评估这些方法。还可以从实验上证明，我们的方法是实时运行的，即使在密集的交通情况下，帧执行时间也不超过30毫秒，从而可以直接实施自动驾驶汽车。
Radar sensors have become an important part of the perception sensor suite due to their long range and their ability to work in adverse weather conditions. However, several shortcomings such as large amounts of noise and extreme sparsity of the point cloud result in them not being used to their full potential. In this paper, we present a novel Recursive Least Squares (RLS) based approach to estimate the instantaneous velocity of dynamic objects in real-time that is capable of handling large amounts of noise in the input data stream. We also present an end-to-end pipeline to track extended objects in real-time that uses the computed velocity estimates for data association and track initialisation. The approaches are evaluated using several real-world inspired driving scenarios that test the limits of these algorithms. It is also experimentally proven that our approaches run in real-time with frame execution time not exceeding 30 ms even in dense traffic scenarios, thus allowing for their direct implementation on autonomous vehicles.
