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

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.