论文标题
雷达内特：利用雷达以强大的感知动态对象
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
论文作者
论文摘要
我们解决了在自动驾驶的背景下，将雷达剥削的问题作为雷达以多普勒速度的形式为其他传感器（例如LIDAR或摄像机）提供互补信息。使用雷达的主要挑战是噪声和测量模棱两可，这是现有简单输入或输出融合方法的斗争。为了更好地解决这个问题，我们提出了一种新的解决方案，以利用LiDAR和雷达传感器进行感知。我们的方法称为Radarnet，具有基于体素的早期融合和基于注意力的晚期融合，从数据中学习以利用雷达数据的几何和动态信息。在对象检测和速度估计的任务中，Radarnet在两个大型现实世界数据集上实现了最先进的结果。我们进一步表明，利用雷达提高了检测遥远对象和理解动态对象运动的感知能力。
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity. The main challenges of using Radar are the noise and measurement ambiguities which have been a struggle for existing simple input or output fusion methods. To better address this, we propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion, which learn from data to exploit both geometric and dynamic information of Radar data. RadarNet achieves state-of-the-art results on two large-scale real-world datasets in the tasks of object detection and velocity estimation. We further show that exploiting Radar improves the perception capabilities of detecting faraway objects and understanding the motion of dynamic objects.
