In this work, we present RadCloud, a novel real time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds can then be used for accurate environmental mapping, navigating unknown environments, and other robotics tasks. While high-resolution sensing using radar data has been previously reported, existing methods cannot be used on most UAVs, which have limited computational power and energy; thus, existing demonstrations focus on offline radar processing. RadCloud overcomes these challenges by using a radar configuration with 1/4th of the range resolution and employing a deep learning model with 2.25x fewer parameters. Additionally, RadCloud utilizes a novel chirp-based approach that makes obtained point clouds resilient to rapid movements (e.g., aggressive turns or spins), which commonly occur during UAV flights. In real-world experiments, we demonstrate the accuracy and applicability of RadCloud on commercially available UAVs and UGVs, with off-the-shelf radar platforms on-board.

翻译：本文提出RadCloud，一种新颖的实时框架，可直接从资源受限平台（常见于无人机和无人地面车辆）的低分辨率雷达帧中获取高分辨率类激光雷达2D点云；此类点云可用于精确环境建图、未知环境导航及其他机器人任务。尽管已有研究报道了利用雷达数据实现高分辨率感知，但现有方法无法应用于多数计算能力与能量受限的无人机系统，因此现有演示主要聚焦离线雷达处理。RadCloud通过采用距离分辨率降低至1/4的雷达配置，并运用参数量减少2.25倍的深度学习模型克服了上述挑战。此外，RadCloud利用一种新型啁啾基方法，使获取的点云对无人机飞行中常见的快速运动（如剧烈转向或旋转）具有鲁棒性。通过真实世界实验，我们在搭载商用雷达平台的现成无人机与无人地面车辆上验证了RadCloud的精度与适用性。