Cross-modal semantic segmentation for indoor environmental perception using single-chip millimeter-wave radar raw data
This addresses indoor perception challenges for emergency responders, though it appears incremental as it builds on U-Net with spatial attention.
The paper tackles indoor environmental perception for firefighting/rescue by proposing a cross-modal semantic segmentation model using single-chip millimeter-wave radar raw data, achieving more intuitive and accurate representations with minimal azimuth impact.
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.