Transformer based 3D semantic segmentation of urban bicycle infrastructure

Abstract

ABSTRACT This work investigates Point Cloud Semantic Segmentation (PCSS) to detect bicycle paths from 3D point cloud data of bicycle-mounted LiDARs. For the task of PCSS, an existing Convolutional Neural Network architecture (CNN) is first pre-trained on the Semantic KITTI data set, an important data set for PCSS in LiDAR scans of driving situations. Furthermore, a new, semantically-labelled 3D LiDAR data set, the Salzburg Bicycle LiDAR Data Set (SBLD), is presented, which consists of 16,008 point clouds recorded by a ROS2-enabled sensor bicycle with five 3D LiDARs covering every direction. After fine-tuning the CNN on the SBLD train set, the segmentation performance is evaluated on the SBLD test set. The CNN shows promising results in recognising bike paths, vegetation, terrain and buildings in the SBLD. To further push the segmentation performance, the existing CNN is enhanced with self-attention blocks. The evaluation of this enhanced architecture shows a performance gain of 2.79% points compared to the original architecture. The proposed segmentation approach shows practical potential for detecting bike paths. The SBLD is provided to the scientific community to further drive research in the field of sensor bicycles.