DenserRadar: A 4D millimeter-wave radar point cloud detector based on dense LiDAR point clouds
CoRR(2024)
Abstract
The 4D millimeter-wave (mmWave) radar, with its robustness in extreme
environments, extensive detection range, and capabilities for measuring
velocity and elevation, has demonstrated significant potential for enhancing
the perception abilities of autonomous driving systems in corner-case
scenarios. Nevertheless, the inherent sparsity and noise of 4D mmWave radar
point clouds restrict its further development and practical application. In
this paper, we introduce a novel 4D mmWave radar point cloud detector, which
leverages high-resolution dense LiDAR point clouds. Our approach constructs
dense 3D occupancy ground truth from stitched LiDAR point clouds, and employs a
specially designed network named DenserRadar. The proposed method surpasses
existing probability-based and learning-based radar point cloud detectors in
terms of both point cloud density and accuracy on the K-Radar dataset.
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