用单芯片毫米波雷达实现室内环境语义分割，抗方位角干扰强
Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data
- 基于U-Net改进，融合空间注意力机制提升分割精度
- 在距离增加时性能下降，但通过模型设计可有效缓解
- 使用雷达原始数据中的RD张量比ADC数据更合适
针对消防救援场景下的室内环境感知需求，提出一种基于单芯片毫米波雷达的跨模态语义分割模型。为高效获取高质量标签，引入利用LiDAR点云与占据栅格图的自动标注方法。所提模型基于U-Net架构，嵌入空间注意力模块以增强性能。实验表明，该跨模态语义分割能提供更直观、准确的室内环境表征；相比传统方法，其分割性能受方位角影响极小。尽管距离增大导致性能下降，但可通过合理模型设计缓解。此外发现，直接使用原始ADC数据效果不佳，相较之下，使用RD张量作为输入更适合本模型。
原文摘要 · Abstract (English)
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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