2026-05-28T00:19:22Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/107162026-02-03T11:16:02Zcom_10630_2254col_10630_37953

Mandow, Anthony Cantador, Tomás J. Reina-Terol, Antonio Jesús Martínez, Jorge L. Morales-Rodríguez, Jesús García-Cerezo, Alfonso José 2015-11-24T08:34:47Z 2016-11-21T05:00:03Z 2015-11-24 http://hdl.handle.net/10630/10716 The paper addresses terrain modeling for mobile robots with fuzzy elevation maps by improving computational speed and performance over previous work on fuzzy terrain identification from a three-dimensional (3D) scan. To this end, spherical sub-sampling of the raw scan is proposed to select training data that does not filter out salient obstacles. Besides, rule structure is systematically defined by considering triangular sets with an unevenly distributed standard fuzzy partition and zero order Sugeno-type consequents. This structure, which favors a faster training time and reduces the number of rule parameters, also serves to compute a fuzzy reliability mask for the continuous fuzzy surface. The paper offers a case study using a Hokuyo-based 3D rangefinder to model terrain with and without outstanding obstacles. Performance regarding error and model size is compared favorably with respect to a solution that uses quadric-based surface simplification (QSlim). eng open access by-nc-nd Robots Building Fuzzy Elevation Maps from a Ground-based 3D Laser Scan for Outdoor Mobile Robots journal article