Egomaster: A central ego motion estimation for driver assist systems

Michael Baer, Mohamed Essayed Bouzouraa, Christopher Demiral, Ulrich Hofmann, Stefan Gies, Klaus Diepold

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

In this paper we present an approach for a central ego motion estimation with standard and near-series sensors for advanced driver assist systems. The two main contributions of this article are the provision of all variables relative to the road surface and the fusion of several sensors and perception modules providing information of ego localization. Thus we employ a discrete Kalman filter to consider the standard lift sensors of the suspension and to take the radial tire deflection into account. Additionally, a reference sensor set is introduced which enables to evaluate all measured and estimated states, even those which are not related to the vector of gravity. The validation by the reference sensor set demonstrates the accurrancy and the efficiency of the approach.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Control and Automation, ICCA 2009
Pages1708-1715
Number of pages8
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Control and Automation, ICCA 2009 - Christchurch, New Zealand
Duration: 9 Dec 200911 Dec 2009

Publication series

Name2009 IEEE International Conference on Control and Automation, ICCA 2009

Conference

Conference2009 IEEE International Conference on Control and Automation, ICCA 2009
Country/TerritoryNew Zealand
CityChristchurch
Period9/12/0911/12/09

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