RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost Radars for Aerial and Ground Vehicles
<|reference_start|>RadCloud: Real-Time High-Resolution Point Cloud Generation Using Low-Cost Radars for Aerial and Ground Vehicles: 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.<|reference_end|>
@article{hunt2024radcloud:,
    title={RadCloud: Real-Time High-Resolution Point Cloud Generation Using
  Low-Cost Radars for Aerial and Ground Vehicles},
    author={David Hunt, Shaocheng Luo, Amir Khazraei, Xiao Zhang, Spencer
  Hallyburton, Tingjun Chen, Miroslav Pajic},
    journal={arXiv preprint arXiv:2403.05964},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05964},
    primaryClass={cs.RO eess.SP}
}