H filtering convergence and it's application to SLAM

Hamzah Ahmad, Toru Namerikawa

研究成果: Conference contribution

10 引用 (Scopus)

抄録

KF-SLAM(Kalman filter-SLAM) have been used as a popular solution by researchers in many SLAM application. Nevertheless, it shortcomings of assumption for Gaussian noise limited its efficiency and demand researcher to consider better filter and algorithm to achieve a promising result of estimation. In this paper, we proposed one of its family, the H filter-based SLAM to determine its competency for SLAM problem. Unlike Kalman filter, H filter able to work in an unknown statistical noise behavior and thus more robust. It rely on a guess that the noise is in bounded energy and does not require a priori knowledge about the system. Therefore, we proposed the H filter as other available technique to infer the location for both robot and landmarks while simultaneously building the map. From the results of simulation, H filter produces better outcome than the Kalman filter especially in the linear case estimation. As a result, H filter may provides another available estimation methods with the capability to ensure and improve estimation for the robotic mapping problem especially in SLAM.

元の言語English
ホスト出版物のタイトルICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
ページ2875-2880
ページ数6
出版物ステータスPublished - 2009
イベントICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
継続期間: 2009 8 182009 8 21

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
Japan
Fukuoka
期間09/8/1809/8/21

Fingerprint

Kalman filters
Robotics
Robots

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

これを引用

Ahmad, H., & Namerikawa, T. (2009). H filtering convergence and it's application to SLAM. : ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 2875-2880). [5333855]

H filtering convergence and it's application to SLAM. / Ahmad, Hamzah; Namerikawa, Toru.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 2875-2880 5333855.

研究成果: Conference contribution

Ahmad, H & Namerikawa, T 2009, H filtering convergence and it's application to SLAM. : ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings., 5333855, pp. 2875-2880, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, Japan, 09/8/18.
Ahmad H, Namerikawa T. H filtering convergence and it's application to SLAM. : ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 2875-2880. 5333855
Ahmad, Hamzah ; Namerikawa, Toru. / H filtering convergence and it's application to SLAM. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 2875-2880
@inproceedings{ef611a97845f4d46879425648edd9c2e,
title = "H∞ filtering convergence and it's application to SLAM",
abstract = "KF-SLAM(Kalman filter-SLAM) have been used as a popular solution by researchers in many SLAM application. Nevertheless, it shortcomings of assumption for Gaussian noise limited its efficiency and demand researcher to consider better filter and algorithm to achieve a promising result of estimation. In this paper, we proposed one of its family, the H ∞ filter-based SLAM to determine its competency for SLAM problem. Unlike Kalman filter, H∞ filter able to work in an unknown statistical noise behavior and thus more robust. It rely on a guess that the noise is in bounded energy and does not require a priori knowledge about the system. Therefore, we proposed the H∞ filter as other available technique to infer the location for both robot and landmarks while simultaneously building the map. From the results of simulation, H ∞ filter produces better outcome than the Kalman filter especially in the linear case estimation. As a result, H∞ filter may provides another available estimation methods with the capability to ensure and improve estimation for the robotic mapping problem especially in SLAM.",
keywords = "Estimation, H filter, Kalman filter, SLAM",
author = "Hamzah Ahmad and Toru Namerikawa",
year = "2009",
language = "English",
isbn = "9784907764333",
pages = "2875--2880",
booktitle = "ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings",

}

TY - GEN

T1 - H∞ filtering convergence and it's application to SLAM

AU - Ahmad, Hamzah

AU - Namerikawa, Toru

PY - 2009

Y1 - 2009

N2 - KF-SLAM(Kalman filter-SLAM) have been used as a popular solution by researchers in many SLAM application. Nevertheless, it shortcomings of assumption for Gaussian noise limited its efficiency and demand researcher to consider better filter and algorithm to achieve a promising result of estimation. In this paper, we proposed one of its family, the H ∞ filter-based SLAM to determine its competency for SLAM problem. Unlike Kalman filter, H∞ filter able to work in an unknown statistical noise behavior and thus more robust. It rely on a guess that the noise is in bounded energy and does not require a priori knowledge about the system. Therefore, we proposed the H∞ filter as other available technique to infer the location for both robot and landmarks while simultaneously building the map. From the results of simulation, H ∞ filter produces better outcome than the Kalman filter especially in the linear case estimation. As a result, H∞ filter may provides another available estimation methods with the capability to ensure and improve estimation for the robotic mapping problem especially in SLAM.

AB - KF-SLAM(Kalman filter-SLAM) have been used as a popular solution by researchers in many SLAM application. Nevertheless, it shortcomings of assumption for Gaussian noise limited its efficiency and demand researcher to consider better filter and algorithm to achieve a promising result of estimation. In this paper, we proposed one of its family, the H ∞ filter-based SLAM to determine its competency for SLAM problem. Unlike Kalman filter, H∞ filter able to work in an unknown statistical noise behavior and thus more robust. It rely on a guess that the noise is in bounded energy and does not require a priori knowledge about the system. Therefore, we proposed the H∞ filter as other available technique to infer the location for both robot and landmarks while simultaneously building the map. From the results of simulation, H ∞ filter produces better outcome than the Kalman filter especially in the linear case estimation. As a result, H∞ filter may provides another available estimation methods with the capability to ensure and improve estimation for the robotic mapping problem especially in SLAM.

KW - Estimation

KW - H filter

KW - Kalman filter

KW - SLAM

UR - http://www.scopus.com/inward/record.url?scp=77951138102&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951138102&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:77951138102

SN - 9784907764333

SP - 2875

EP - 2880

BT - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

ER -