Random sampling and probabilistic consensus for identifying outliers in road surface datasets
by Savio Pereira; John Ferris
International Journal of Vehicle Systems Modelling and Testing (IJVSMT), Vol. 14, No. 2/3, 2020

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.

Online publication date: Wed, 09-Dec-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Systems Modelling and Testing (IJVSMT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com