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.

翻译：可靠的感知必须能够应对恶劣环境条件的挑战。因此，近期研究重点转向在感知应用中除了使用相机和激光雷达传感器外，还引入雷达传感器。然而，雷达点云的稀疏性与数据可用性不足仍是当前感知方法面临的难题。为应对这些挑战，本文提出一种新型图神经网络，它不仅利用点本身的信息，还挖掘点与点之间的关联关系。该模型设计为同时考虑点特征与点对特征，并将其嵌入图的边结构中。此外，本文提出一种通用的变换不变性实现方法，该方法对未见场景具有鲁棒性，同时可缓解数据有限的问题。该变换不变性通过不变数据表示而非不变模型架构实现，因此可推广至其他方法。所提出的RadarGNN模型在RadarScenes数据集上全面超越此前所有方法。同时，本文还研究了不同不变性对目标检测与语义分割质量的影响。相关代码已作为开源软件发布在https://github.com/TUMFTM/RadarGNN。