利用单芯片毫米波雷达原始数据进行室内环境感知的跨模态语义分割
Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data
AI总结：
面向消防救援室内感知，提出基于单芯片毫米波雷达与 U-Net 的跨模态语义分割模型，结合空间注意力和 LiDAR 自动标注，并验证 RD 张量优于 RA 张量及原始 ADC 输入。
摘要：
在消防与救援行动背景下，提出并讨论了一种基于单芯片毫米波（mmWave）雷达、用于室内环境感知的跨模态语义分割模型。为高效获取高质量标签，介绍了一种利用 LiDAR 点云和占据栅格地图的自动标签生成方法。所提出的分割模型基于 U-Net，并加入空间注意力模块，从而提升了模型性能。结果表明，跨模态语义分割能够更直观、准确地表征室内环境。与传统方法不同，该模型的分割性能受方位角影响很小。尽管性能会随距离增加而下降，但这可通过设计良好的模型得到缓解。此外，研究发现使用原始 ADC 数据作为输入是无效的；与 RA 张量相比，RD 张量更适合所提出的模型。
英文摘要：
In the context of firefighting and rescue operations, a cross-modal semantic segmentation model based on a single-chip millimeter-wave (mmWave) radar for indoor environmental perception is proposed and discussed. To efficiently obtain high-quality labels, an automatic label generation method utilizing LiDAR point clouds and occupancy grid maps is introduced. The proposed segmentation model is based on U-Net. A spatial attention module is incorporated, which enhanced the performance of the mode. The results demonstrate that cross-modal semantic segmentation provides a more intuitive and accurate representation of indoor environments. Unlike traditional methods, the model's segmentation performance is minimally affected by azimuth. Although performance declines with increasing distance, this can be mitigated by a well-designed model. Additionally, it was found that using raw ADC data as input is ineffective; compared to RA tensors, RD tensors are more suitable for the proposed model.
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