论文标题
自主赛车的准确，低延迟的视觉感知：挑战，机制和实用解决方案
Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions
论文作者
论文摘要
自主赛车为测试安全关键感知管道的机会提供了机会。本文介绍了针对DUT18 Driverless（DUT18D），应用最先进的计算机视觉算法的实用挑战和解决方案，以在所有配方赛车比赛中为其赛车的所有配方赛车竞争，建立了一个高延长的，高准确的感知系统。 DUT18D的关键组件包括基于Yolov3的对象检测，姿势估计以及其双立体电视/Monovision摄像头设置上的时间同步。我们重点介绍了将感知CNN适应赛车领域所需的修改，用于姿势估计的损失功能的改进以及子微秒摄像头同步的方法以及其他改进的方法。我们对系统进行了彻底的实验评估，证明了其在现实世界赛车场景中的准确性和低延迟。
Autonomous racing provides the opportunity to test safety-critical perception pipelines at their limit. This paper describes the practical challenges and solutions to applying state-of-the-art computer vision algorithms to build a low-latency, high-accuracy perception system for DUT18 Driverless (DUT18D), a 4WD electric race car with podium finishes at all Formula Driverless competitions for which it raced. The key components of DUT18D include YOLOv3-based object detection, pose estimation, and time synchronization on its dual stereovision/monovision camera setup. We highlight modifications required to adapt perception CNNs to racing domains, improvements to loss functions used for pose estimation, and methodologies for sub-microsecond camera synchronization among other improvements. We perform a thorough experimental evaluation of the system, demonstrating its accuracy and low-latency in real-world racing scenarios.
