TransGSnet: transformer-embedded ground segmentation of point cloud for rough roads

Abstract

Despite comprehensive works on point cloud ground segmentation for flat roads, research on rough roads has rarely been conducted due to dataset scarcity. To study point cloud ground segmentation on rough roads, in this paper, we provide a synthetic geometric transformation of flat roads motivated by the investigation of real-world rough roads. Our proposed TransGSnet framework consists of two modules: the pillar feature extractor, which turns a raw point cloud into the pseudo image as an intermediate representation, and the transformer-based segmentation network to perform ground segmentation. Specifically, our segmentation network exploits the U-Net architecture and includes three sub-modules: Transformer, mobile block (MB), and convolutional block attention module (CBAM). We thoroughly evaluate our framework in experiments, including comparisons against state-of-the-art approaches on semanticKITTI and the synthetic rough road dataset, respectively. As a result, our framework shows a great trade-off of performance cost.