2026-06-01T14:39:50Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/160622026-02-03T12:19:26Zcom_10630_2254col_10630_37959

Ground Extraction from 3D Lidar Point Clouds Pomares, Antonio Martínez, Jorge L. Mandow, Anthony Martínez-Sánchez, María Alcázar Morán, Mariano Aprendizaje automático (Inteligencia artificial) Ground 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. 2018-06-28T11:51:06Z 2018-06-28T11:51:06Z 2018-06-28T11:51:06Z 2018 conference output https://hdl.handle.net/10630/16062 spa 26th Mediterranean Conference on Control and Automation Zadar, Croatia 19-22 de junio de 2018 open access IEEE