Coarse-to-fine classification of road infrastructure elements from mobile point clouds using symmetric ensemble point network and euclidean cluster extraction

Abstract

Classifying point clouds obtained from mobile laser scanning of road environments is a fundamental yet challenging problem for road asset management and unmanned vehicle navigation. Deep learning networks need no prior knowledge to classify multiple objects, but often generate a certain amount of false predictions. However, traditional clustering methods often involve leveraging a priori knowledge, but may lack generalisability compared to deep learning networks. This paper presents a classification method that coarsely classifies multiple objects of road infrastructure with a symmetric ensemble point (SEP) network and then refines the results with a Euclidean cluster extraction (ECE) algorithm. The SEP network applies a symmetric function to capture relevant structural features at different scales and select optimal sub-samples using an ensemble method. The ECE subsequently adjusts points that have been predicted incorrectly by the first step. The experimental results indicate that this method effectively extracts six types of road infrastructure elements: road surfaces, buildings, walls, traffic signs, trees and streetlights. The overall accuracy of the SEP-ECE method improves by 3.97% with respect to PointNet. The achieved average classification accuracy is approximately 99.74%, which is suitable for practical use in transportation network management.