Simultaneous Localization and Odometry Calibration for Mobile Robot

Agostino Martinelli; Nicola Tomatis; Adriana Tapus; Roland Siegwart

Simultaneous Localization and Odometry Calibration for Mobile Robot

Agostino Martinelli
, Nicola Tomatis
, Adriana Tapus
, Roland Siegwart

Ecole Polytechnique Fédérale de Lausanne (EPFL)
ENAC-IIC-GEL

Research output: Contribution to conference › Paper › peer-review

84 Citations (Scopus)

Abstract

This paper presents both the theory and the first experimental results of a new method which allows simultaneously estimating of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic component is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.

Original language	English
Pages	1499-1504
Number of pages	6
Publication status	Published - 26 Dec 2003
Externally published	Yes
Event	2003 IEEE/RSJ International Conference on Intelligent Robots and Systems - Las Vegas, NV, United States Duration: 27 Oct 2003 → 31 Oct 2003

Conference

Conference	2003 IEEE/RSJ International Conference on Intelligent Robots and Systems
Country/Territory	United States
City	Las Vegas, NV
Period	27/10/03 → 31/10/03

Keywords

Kalman filter
Odometry Learning
Robot Navigation

Cite this

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title = "Simultaneous Localization and Odometry Calibration for Mobile Robot",

abstract = "This paper presents both the theory and the first experimental results of a new method which allows simultaneously estimating of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic component is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.",

keywords = "Kalman filter, Odometry Learning, Robot Navigation",

author = "Agostino Martinelli and Nicola Tomatis and Adriana Tapus and Roland Siegwart",

year = "2003",

month = dec,

day = "26",

language = "English",

pages = "1499--1504",

note = "2003 IEEE/RSJ International Conference on Intelligent Robots and Systems ; Conference date: 27-10-2003 Through 31-10-2003",

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TY - CONF

T1 - Simultaneous Localization and Odometry Calibration for Mobile Robot

AU - Martinelli, Agostino

AU - Tomatis, Nicola

AU - Tapus, Adriana

AU - Siegwart, Roland

PY - 2003/12/26

Y1 - 2003/12/26

N2 - This paper presents both the theory and the first experimental results of a new method which allows simultaneously estimating of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic component is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.

AB - This paper presents both the theory and the first experimental results of a new method which allows simultaneously estimating of the robot configuration and the odometry error (both systematic and non-systematic) during the mobile robot navigation. The estimation of the non-systematic components is carried out through an augmented Kalman filter which estimates a state containing the robot configuration and the parameters characterizing the systematic component of the odometry error. It uses encoder readings as inputs and the readings from a laser range finder as observations. The estimation of the non-systematic component is carried out through another Kalman filter where the observations are obtained by two subsequent robot configurations provided by the previous augmented Kalman filter.

KW - Kalman filter

KW - Odometry Learning

KW - Robot Navigation

M3 - Paper

AN - SCOPUS:0346779051

SP - 1499

EP - 1504

T2 - 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems

Y2 - 27 October 2003 through 31 October 2003

ER -

Simultaneous Localization and Odometry Calibration for Mobile Robot

Abstract

Conference

Keywords

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Cite this