A new Approach for Kalman filtering on Mobile Robots in the presence of uncertainties

Thomas Dall Larsen; Nils Axel Andersen; Ole Ravn

doi:10.1109/CCA.1999.801002

A new Approach for Kalman filtering on Mobile Robots in the presence of uncertainties

Thomas Dall Larsen, Nils Axel Andersen, Ole Ravn

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Abstract

In many practical Kalman filter applications, the quantity of most significance for the estimation error is the process noise matrix. When filters are stabilized or performance is sought to be improved, tuning of this matrix is the most common method. This tuning process cannot be done before the filter is implemented, as it is primarily made necessary by modelling errors. In this paper, two different methods for modelling the process noise are described and evaluated; a traditional one based on Gaussian noise models and a new one based on propagating modelling uncertainties. We discuss which method to use and how to tune the filter to achieve the lowest estimation error.

Original language	English
Title of host publication	Proceedings of IEEE Conference on Control Applications
Volume	2
Place of Publication	Hawaii
Publisher	IEEE
Publication date	1999
ISBN (Print)	0-7803-5446-X
DOIs	https://doi.org/10.1109/CCA.1999.801002
Publication status	Published - 1999
Event	1999 IEEE International Conference on Control Applications - Hawaii, HI, United States Duration: 22 Aug 1999 → 27 Aug 1999 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6503

Conference

Conference	1999 IEEE International Conference on Control Applications
Country/Territory	United States
City	Hawaii, HI
Period	22/08/1999 → 27/08/1999
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6503

Bibliographical note

Copyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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title = "A new Approach for Kalman filtering on Mobile Robots in the presence of uncertainties",

abstract = "In many practical Kalman filter applications, the quantity of most significance for the estimation error is the process noise matrix. When filters are stabilized or performance is sought to be improved, tuning of this matrix is the most common method. This tuning process cannot be done before the filter is implemented, as it is primarily made necessary by modelling errors. In this paper, two different methods for modelling the process noise are described and evaluated; a traditional one based on Gaussian noise models and a new one based on propagating modelling uncertainties. We discuss which method to use and how to tune the filter to achieve the lowest estimation error.",

author = "Larsen, {Thomas Dall} and Andersen, {Nils Axel} and Ole Ravn",

note = "Copyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 1999 IEEE International Conference on Control Applications ; Conference date: 22-08-1999 Through 27-08-1999",

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AU - Andersen, Nils Axel

AU - Ravn, Ole

N1 - Copyright 1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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N2 - In many practical Kalman filter applications, the quantity of most significance for the estimation error is the process noise matrix. When filters are stabilized or performance is sought to be improved, tuning of this matrix is the most common method. This tuning process cannot be done before the filter is implemented, as it is primarily made necessary by modelling errors. In this paper, two different methods for modelling the process noise are described and evaluated; a traditional one based on Gaussian noise models and a new one based on propagating modelling uncertainties. We discuss which method to use and how to tune the filter to achieve the lowest estimation error.

AB - In many practical Kalman filter applications, the quantity of most significance for the estimation error is the process noise matrix. When filters are stabilized or performance is sought to be improved, tuning of this matrix is the most common method. This tuning process cannot be done before the filter is implemented, as it is primarily made necessary by modelling errors. In this paper, two different methods for modelling the process noise are described and evaluated; a traditional one based on Gaussian noise models and a new one based on propagating modelling uncertainties. We discuss which method to use and how to tune the filter to achieve the lowest estimation error.

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DO - 10.1109/CCA.1999.801002

M3 - Article in proceedings

SN - 0-7803-5446-X

VL - 2

BT - Proceedings of IEEE Conference on Control Applications

PB - IEEE

CY - Hawaii

T2 - 1999 IEEE International Conference on Control Applications

Y2 - 22 August 1999 through 27 August 1999

ER -

A new Approach for Kalman filtering on Mobile Robots in the presence of uncertainties

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