Cumulative error estimation from noisy relative measurements

Feihu Zhang, Carsten Simon, Guang Chen, Christian Buckl, Alois Knoll

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

11 Scopus citations

Abstract

Odometry is important for autonomous vehicle in scenarios where GPS is either unavailable or only intermittently available. However, in a large scale environment, it often generalizes unbounded cumulative error when the vehicle unconsciously moves. This paper analyzes how the cumulative error grows according to the noisy relative measurements. An unbounded drift model is proposed to represent the cumulative error, where its probability distribution is described by the corresponding expectation and variance. Compared to other approaches, it presents a recursive cumulative error expression in absence of the true positions, which has great potentials in various domains, e. g. path planning, odmetry based localization. Both experiments and cases are conducted to not only verify the accuracy of the proposed model, but also illustrate the potentials in related domains.

Original languageEnglish
Title of host publication2013 16th International IEEE Conference on Intelligent Transportation Systems
Subtitle of host publicationIntelligent Transportation Systems for All Modes, ITSC 2013
Pages1422-1429
Number of pages8
DOIs
StatePublished - 2013
Event2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013 - The Hague, Netherlands
Duration: 6 Oct 20139 Oct 2013

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Conference

Conference2013 16th International IEEE Conference on Intelligent Transportation Systems: Intelligent Transportation Systems for All Modes, ITSC 2013
Country/TerritoryNetherlands
CityThe Hague
Period6/10/139/10/13

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