  Scene understanding is crucial for autonomous robots in dynamic environments
for making future state predictions, avoiding collisions, and path planning.
Camera and LiDAR perception made tremendous progress in recent years, but face
limitations under adverse weather conditions. To leverage the full potential of
multi-modal sensor suites, radar sensors are essential for safety critical
tasks and are already installed in most new vehicles today. In this paper, we
address the problem of semantic segmentation of moving objects in radar point
clouds to enhance the perception of the environment with another sensor
modality. Instead of aggregating multiple scans to densify the point clouds, we
propose a novel approach based on the self-attention mechanism to accurately
perform sparse, single-scan segmentation. Our approach, called Gaussian Radar
Transformer, includes the newly introduced Gaussian transformer layer, which
replaces the softmax normalization by a Gaussian function to decouple the
contribution of individual points. To tackle the challenge of the transformer
to capture long-range dependencies, we propose our attentive up- and
downsampling modules to enlarge the receptive field and capture strong spatial
relations. We compare our approach to other state-of-the-art methods on the
RadarScenes data set and show superior segmentation quality in diverse
environments, even without exploiting temporal information.
