3D CNN-based semantic labeling approach for mobile laser scanning data

Abstract

In this paper, we introduce a 3D convolutional neural network (CNN)-based method to segment point clouds obtained by mobile laser scanning (MLS) sensors into nine different semantic classes, which can be used for high definition city map generation. The main purpose of semantic point labeling is to provide a detailed and reliable background map for self-driving vehicles (SDV), which indicates the roads and various landmark objects for navigation and decision support of SDVs. Our approach considers several practical aspects of raw MLS sensor data processing, including the presence of diverse urban objects, varying point density, and strong measurement noise of phantom effects caused by objects moving concurrently with the scanning platform. We also provide a new manually annotated MLS benchmark set called SZTAKI CityMLS, which is used to evaluate the proposed approach, and to compare our solution to various reference techniques proposed for semantic point cloud segmentation. Apart from point level validation we also present a case study on Lidar-based accurate self-localization of SDVs in the segmented MLS map.