  A reliable perception has to be robust against challenging environmental
conditions. Therefore, recent efforts focused on the use of radar sensors in
addition to camera and lidar sensors for perception applications. However, the
sparsity of radar point clouds and the poor data availability remain
challenging for current perception methods. To address these challenges, a
novel graph neural network is proposed that does not just use the information
of the points themselves but also the relationships between the points. The
model is designed to consider both point features and point-pair features,
embedded in the edges of the graph. Furthermore, a general approach for
achieving transformation invariance is proposed which is robust against unseen
scenarios and also counteracts the limited data availability. The
transformation invariance is achieved by an invariant data representation
rather than an invariant model architecture, making it applicable to other
methods. The proposed RadarGNN model outperforms all previous methods on the
RadarScenes dataset. In addition, the effects of different invariances on the
object detection and semantic segmentation quality are investigated. The code
is made available as open-source software under
https://github.com/TUMFTM/RadarGNN.
