Fusion Segmentation Network Guided by Adaptive Sampling Radius and Channel Attention Mechanism Module for MLS Point Clouds

Abstract

Road high-precision mobile LiDAR measurement point clouds are the digital infrastructures for high-precision maps, autonomous driving, digital twins, etc. High-precision automated semantic segmentation of road point clouds is a crucial research direction. Aiming at the problem of low semantic segmentation accuracy of existing deep learning networks for inhomogeneous sparse point clouds of mobile LiDAR system measurements (MLS), a deep learning method that adaptively adjusts the sampling radius of region groups according to the point clouds density is proposed. We construct a deep learning road point clouds dataset based on a self-developed mobile LiDAR system to train and test road point clouds semantic segmentation. The overall accuracy of the method for road point clouds segmentation is 98.08%, with an overall mIOU of 0.73 and mIOUs of 0.99, 0.983, 0.99, 0.66, and 0.51 for roads, guardrails, signs, streetlights, and lane lines, respectively. The experimental result shows that the method can achieve more accurate segmentation for inhomogeneous sparse road point clouds of mobile LiDAR systems. Compared with the existing methods, the segmentation accuracy is significantly improved.