论文标题
我们是否需要在旋转雷达导航中补偿运动失真和多普勒效应？
Do We Need to Compensate for Motion Distortion and Doppler Effects in Spinning Radar Navigation?
论文作者
论文摘要
为了应对不利天气条件（例如雨水和雪）的挑战，正在重新审视雷达，以此作为对视力和激光态的平行感应方式。最近的作品在将旋转雷达应用于进程和放置识别方面取得了巨大进展。但是，到目前为止，这些作品忽略了运动失真和多普勒对基于旋转雷达的导航的影响，这在速度可能很高的自动驾驶汽车域中可能很重要。在这项工作中，我们使用牛津雷达机器人数据集和使用我们自己的数据处理平台使用牛津雷达机器人数据集和度量定位来证明这些扭曲对雷达进进仪的影响。我们重新审视一个轻巧的估计器，该估计器可以在考虑两种效果的同时恢复一对雷达扫描之间的运动。我们的结论是，运动失真和多普勒效应在旋转雷达导航的不同方面都很重要，而前者比后者更为突出。可以在以下网址找到该项目的代码
In order to tackle the challenge of unfavorable weather conditions such as rain and snow, radar is being revisited as a parallel sensing modality to vision and lidar. Recent works have made tremendous progress in applying spinning radar to odometry and place recognition. However, these works have so far ignored the impact of motion distortion and Doppler effects on spinning-radar-based navigation, which may be significant in the self-driving car domain where speeds can be high. In this work, we demonstrate the effect of these distortions on radar odometry using the Oxford Radar RobotCar Dataset and metric localization using our own data-taking platform. We revisit a lightweight estimator that can recover the motion between a pair of radar scans while accounting for both effects. Our conclusion is that both motion distortion and the Doppler effect are significant in different aspects of spinning radar navigation, with the former more prominent than the latter. Code for this project can be found at: https://github.com/keenan-burnett/yeti_radar_odometry
