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.

翻译：针对消防救援场景，本文提出并探讨了一种基于单芯片毫米波雷达的跨模态语义分割模型，用于室内环境感知。为高效获取高质量标注，引入了一种利用激光雷达点云与占据栅格图的自动标注生成方法。所提出的分割模型基于U-Net架构，并嵌入了空间注意力模块以提升模型性能。实验结果表明，跨模态语义分割能提供更直观且准确的室内环境表征。与传统方法不同，该模型的分割性能受方位角影响极小。虽然性能随距离增加而下降，但可通过精心设计的模型予以缓解。此外，研究发现直接使用原始ADC数据作为输入效果不佳；相较于RA张量，RD张量更适用于所提出的模型。