Circumventing dynamic modeling

evaluation of the error-state Kalman filter applied to mobile robot localization

Stergios I. Roumeliotis, Gaurav S. Sukhatme, George A. Bekey

Research output: Contribution to journalArticle

100 Citations (Scopus)

Abstract

The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carriers an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.

Original languageEnglish (US)
Pages (from-to)1656-1663
Number of pages8
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2
StatePublished - Jan 1 1999

Fingerprint

Kalman filters
Mobile robots
Sensors

Cite this

@article{5c3c3f09cd4b48138fdba9d032301f78,
title = "Circumventing dynamic modeling: evaluation of the error-state Kalman filter applied to mobile robot localization",
abstract = "The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carriers an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.",
author = "Roumeliotis, {Stergios I.} and Sukhatme, {Gaurav S.} and Bekey, {George A.}",
year = "1999",
month = "1",
day = "1",
language = "English (US)",
volume = "2",
pages = "1656--1663",
journal = "Proceedings - IEEE International Conference on Robotics and Automation",
issn = "1050-4729",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Circumventing dynamic modeling

T2 - evaluation of the error-state Kalman filter applied to mobile robot localization

AU - Roumeliotis, Stergios I.

AU - Sukhatme, Gaurav S.

AU - Bekey, George A.

PY - 1999/1/1

Y1 - 1999/1/1

N2 - The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carriers an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.

AB - The mobile robot localization problem is treated as a two-stage iterative estimation process. The attitude is estimated first and is then available for position estimation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when applying gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measurements. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transferred to another mobile platform provided it carriers an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.

UR - http://www.scopus.com/inward/record.url?scp=0032689274&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032689274&partnerID=8YFLogxK

M3 - Article

VL - 2

SP - 1656

EP - 1663

JO - Proceedings - IEEE International Conference on Robotics and Automation

JF - Proceedings - IEEE International Conference on Robotics and Automation

SN - 1050-4729

ER -