3D Object Detection on Synthetic Point Clouds for Railway Applications

Abstract

Accurate detection and classification of objects in 3D point clouds is a central problem in several applications such as autonomous navigation and augmented/virtual reality scenarios. In this paper we present a deep learning strategy for 3D object detection for railway applications based on the VoxelNet model. Due to the lack of publicly available annotated data, we created a virtual railway environment for generating a synthetic annotated railway point cloud dataset. This approach allows to model shapes and locations of target landmarks such as traffic lights and railtracks. The achieved results show that our network learns an effective representation of railway landmarks using only raw LiDAR point clouds, leading to encouraging results and possible future implementations in this research field. We also made the annotated dataset available to the research community at https://gitlab.com/michael.neri/sard-synthetic-annotated-railway-dataset.