论文标题
lix-lidar雷达：公制雷达在先前的激光雷达图上定位
Radar-on-Lidar: metric radar localization on prior lidar maps
论文作者
论文摘要
雷达和激光雷达，由两个不同的范围传感器提供，每个传感器都具有有关移动机器人或自动驾驶的各种感知任务的利弊。在本文中，蒙特卡洛系统用于在2D激光镜头上使用旋转雷达传感器定位机器人。我们首先训练有条件的生成对抗网络，以将原始雷达数据传输到激光雷达数据，并从发电机中获得可靠的雷达点。然后，在蒙特卡洛系统中包括有效的雷达探光仪。结合了探视法的初始猜测，提出了一个测量模型，以匹配雷达数据和先验的激光映射，以进行最终的2D定位。我们证明了在公共多课程数据集中提出的本地化框架的有效性。实验结果表明，我们的系统可以在室外场景中实现长期定位的高精度。
Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes.
