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- W2290522013 abstract "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 are compared favorably with respect to a solution that uses quadric-based surface simplification (QSlim)." @default.
- W2290522013 created "2016-06-24" @default.
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- W2290522013 date "2015-12-02" @default.
- W2290522013 modified "2023-10-01" @default.
- W2290522013 title "Building Fuzzy Elevation Maps from a Ground-Based 3D Laser Scan for Outdoor Mobile Robots" @default.
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- W2290522013 doi "https://doi.org/10.1007/978-3-319-27146-0_3" @default.
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