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).
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