A training dataset for semantic segmentation of urban point cloud map for intelligent vehicles

This paper presents a training dataset of point-wisely labeled global point cloud map (GPCM) in urban area. The labeled GPCM dataset can be used to train machine learning models for point cloud semantic segmentation (PCSS). The trained PCSS machine learning model can classify the semantic class of urban GPCM point-wisely to build a semantic GPCM (SGPCM). The SGPCM can be used to enhance the functionality of many intelligent and autonomous vehicle applications, such as localization, map-based perception, and high-definition (HD) map processing. To maximize the utility of the training dataset for the applications, the semantic classes are designed taking into account the requirements of intelligent and autonomous vehicles in urban area. Therefore, the classes contain urban-related objects, such as road surface markers, curba, and in-tunnel objects, which were not included in other previous datasets. First step of construction process for the PCSS training dataset is a generation of the GPCM in the urban area by using a mobile mapping system (MMS). The points in the generated GPCM with MMS contain information about the position (X, Y, Z) and the surface (R, G, B, I). After building the unlabeled GPCM, each point is labeled with the designed semantic classes thorough semi-automatic and manual labeling. The labeled SGPCM was used to train various state-of-the-art PCSS models to verify the effectiveness of SGPCM train-set for the intelligent and autonomous vehicles.