3D Lidar Point Cloud Segmentation for Automated Driving

Abstract

The use of 3D point clouds (3DPCs) in deep learning (DL) has recently gained popularity due to several applications in fields such as computer vision, autonomous systems, and robotics. DL, as a dominant artificial intelligence approach, has been effectively applied to handle a variety of 3D vision challenges. However, building strong discriminative feature representations from irregular and unordered PCs is difficult. Self-driving systems commonly employ lidar to obtain precise 3D geometric data around vehicles for perception, path planning, and localization. The semantic segmentation of lidar-based PCs is a key activity that must be completed in real time. However, the majority of current convolutional neural network models for 3DPC semantic segmentation are extremely complex and cannot be processed in real time on an embedded platform. In this article, our goal is to offer a comprehensive review of current advances in DL approaches for PC feature representation, including 3DPC segmentation, and future challenges.