RT Conference Proceedings
T1 Ground Extraction from 3D Lidar Point Clouds
A1 Pomares, Antonio
A1 Martínez, Jorge L.
A1 Mandow, Anthony
A1 Martínez-Sánchez, María Alcázar
A1 Morán, Mariano
K1 Aprendizaje automático (Inteligencia artificial)
AB Ground extraction from three-dimensional (3D)range data is a relevant problem for outdoor navigationof 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.
PB IEEE
YR 2018
FD 2018
LK https://hdl.handle.net/10630/16062
UL https://hdl.handle.net/10630/16062
LA spa
NO © 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 worksPomares, 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: Pending
NO This work was partially supported by the Spanish project DPI 2015- 65186-R. The publication has received supportfrom Universidad de Málaga, Campus de Excelencia Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026