  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.
