Title: Random sampling and probabilistic consensus for identifying outliers in road surface datasets

Authors: Savio Pereira; John Ferris

Addresses: Department of Mechanical Engineering, Virginia Tech, Blacksurg, VA, 24060, USA ' Department of Mechanical Engineering, Virginia Tech, Blacksurg, VA, 24060, USA

Abstract: Road surface measurement plays a crucial role in the modelling and simulation of vehicles as the road surface is one of the primary means of excitation. A prevalent technique for measuring road surfaces utilises scanning lasers whose measurements produce a non-uniform, 3-dimensional point cloud representation, in which statistical outliers typically manifest. In this work, a novel, axiomatic, probabilistic method for simultaneously identifying outliers and estimating the road surface height at uniformly spaced grid nodes is developed. The method expands on the concepts used in the seminal model fitting algorithm, random sampling and consensus (RANSAC), to address a situation in which multiple underlying models may exist in a neighbourhood of the data. The proposed method, called random sampling and probabilistic consensus (RSPC), is evaluated on a 2-dimensional simulated road surface dataset containing 60% outliers in order to demonstrate its effectiveness at identifying outliers and simultaneously estimating grid node heights.

Keywords: RANSAC; random sampling and consensus; sampling and consensus; logistic function; heights; grid nodes; RSMS; road surface measurement system; outlier identification; point cloud; scanning lasers; anisotropic; validity.

DOI: 10.1504/IJVSMT.2020.111679

International Journal of Vehicle Systems Modelling and Testing, 2020 Vol.14 No.2/3, pp.133 - 148

Received: 11 Sep 2019
Accepted: 07 Dec 2019
Published online: 09 Dec 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article