Ground Extraction from 3D Lidar Point Clouds

Abstract

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Pomares, A., Martínez, J.L., Mandow, A., Martínez, M.A., Morán, M., Morales, J. Ground extraction from 3D lidar point clouds with the Classification Learner App (2018) 26th Mediterranean Conference on Control and Automation, Zadar, Croatia, June 2018, pp.400-405. DOI: PendingGround extraction from three-dimensional (3D) range data is a relevant problem for outdoor navigation of unmanned ground vehicles. Even if this problem has received attention with specific heuristics and segmentation approaches, identification of ground and non-ground points can benefit from state-of-the-art classification methods, such as those included in the Matlab Classification Learner App. This paper proposes a comparative study of the machine learning methods included in this tool in terms of training times as well as in their predictive performance. With this purpose, we have combined three suitable features for ground detection, which has been applied to an urban dataset with several labeled 3D point clouds. Most of the analyzed techniques achieve good classification results, but only a few offer low training and prediction times.This work was partially supported by the Spanish project DPI 2015- 65186-R. The publication has received support from Universidad de Málaga, Campus de Excelencia Andalucía Tech