Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs -- radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

翻译：无人机依靠摄像头和激光雷达等光学传感器实现自主运行。然而，在恶劣光照、包括雾和烟在内的恶劣天气条件，以及无纹理或透明表面附近，此类光学传感器容易出错。本文提出疑问：是否可能在不依赖光学传感器的情况下飞行无人机，即无人机能否实现盲飞？我们提出了BatMobility，一种用于无人机的轻量级纯毫米波雷达感知系统，消除了对光学传感器的需求。BatMobility为无人机实现了两项核心功能——无线电流量估计（一种基于表面平行多普勒频移的新型FMCW雷达替代光流法）和基于雷达的避障。我们使用商用传感器构建了BatMobility，并将其部署在运行未修改飞行控制器的小型现成四旋翼无人机上作为实时系统。评估表明，在各种场景下，BatMobility实现了与商业级光学传感器相当或更优的性能。