Building Fuzzy Elevation Maps from a Ground-Based 3D Laser Scan for Outdoor Mobile Robots

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 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).

Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

“…Instead of using the adaptive neuro-fuzzy inference system (ANFIS) for identification [16,18,19], least squares fuzzy modeling [29] has been employed to improve computation time. Additionally, cylindrical coordinates (α, r, z) have been employed instead of Cartesian coordinates (x, y, z).…”

“…Moreover, fuzzy elevation maps (FEMs) can operate with measurement uncertainty of the sensors [16,17]. However, fuzzy interpolation can produce unreliable solutions in areas with sparse measurements, and it is necessary to use a fuzzy reliability mask (FRM) for each FEM [18,19].…”