-Mobile robot navigation through natural terrains is a challenging issue with applications such as planetary exploration or search and rescue. This paper proposes navigability assessment of natural terrains scanned from groundbased 3D laser rangefinders. A continuous model of the terrain is obtained as a fuzzy elevation map (FEM). Based on this model, the proposed solution incorporates terrain navigability both in terms of uncertainties of the 3D input data and slope of the fuzzy surface. Moreover, the paper discusses the application of this method for local path planning. For this purpose, the Bug algorithm has been adapted to compute local paths on the navigable region of the FEM. The method has been applied to actual 3D point clouds on two different experimental sites. Abstract-Mobile robot navigation through natural terrains is a challenging issue with applications such as planetary exploration or search and rescue. This paper proposes navigability assessment of natural terrains scanned from ground-based 3D laser rangefinders. A continuous model of the terrain is obtained as a fuzzy elevation map (FEM). Based on this model, the proposed solution incorporates terrain navigability both in terms of uncertainties of the 3D input data and slope of the fuzzy surface. Moreover, the paper discusses the application of this method for local path planning. For this purpose, the Bug algorithm has been adapted to compute local paths on the navigable region of the FEM. The method has been applied to actual 3D point clouds on two different experimental sites.
-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). Keywords: Elevation maps, mobile robots, 3D scanners, fuzzy modeling ___________________________________________________________________________________________________This is a self-archiving copy of the author's accepted manuscript. The final publication is available at Springer via http://link.springer.com/book/10.1007/978-3-319-27149-1. 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 threedimensional (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).
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