SLAM using EKF, EH∞ and mixed EH2/H∞ filter

Kumar Pakki Bharani Chandra; Da-Wei Gu; Ian Postlethwaite

doi:10.1109/ISIC.2010.5612907

SLAM using EKF, EH∞ and mixed EH2/H∞ filter

Kumar Pakki Bharani Chandra, Da-Wei Gu, Ian Postlethwaite

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Citations (Scopus)

Abstract

The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, EKF's are not the best choice for SLAM as they suffer from the assumption of Gaussian noise statistics and linearization errors, which can degrade the performance. H∞ filter is one of the alternative of Kalman filter. This paper investigates three SLAM algorithms: (i) EKF SLAM (ii) extended H∞(EH∞) SLAM and (iii) mixed extended H2/H∞(EH2/H∞) SLAM. A comparison of the three algorithms is given through numerical simulations.

Original language	English
Title of host publication	2010 IEEE International Symposium on Intelligent Control
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	818-823
ISBN (Print)	978-1424453603
DOIs	https://doi.org/10.1109/ISIC.2010.5612907
Publication status	Published - 2010

Access to Document

10.1109/ISIC.2010.5612907

Cite this

@inbook{da77358a439d4864998845b9f25f0462,

title = "SLAM using EKF, EH∞ and mixed EH2/H∞ filter",

abstract = "The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, EKF's are not the best choice for SLAM as they suffer from the assumption of Gaussian noise statistics and linearization errors, which can degrade the performance. H∞ filter is one of the alternative of Kalman filter. This paper investigates three SLAM algorithms: (i) EKF SLAM (ii) extended H∞(EH∞) SLAM and (iii) mixed extended H2/H∞(EH2/H∞) SLAM. A comparison of the three algorithms is given through numerical simulations.",

author = "{Bharani Chandra}, {Kumar Pakki} and Da-Wei Gu and Ian Postlethwaite",

year = "2010",

doi = "10.1109/ISIC.2010.5612907",

language = "English",

isbn = "978-1424453603",

pages = "818--823",

booktitle = "2010 IEEE International Symposium on Intelligent Control",

publisher = "IEEE",

address = "United States",

}

TY - CHAP

T1 - SLAM using EKF, EH∞ and mixed EH2/H∞ filter

AU - Bharani Chandra, Kumar Pakki

AU - Gu, Da-Wei

AU - Postlethwaite, Ian

PY - 2010

Y1 - 2010

N2 - The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, EKF's are not the best choice for SLAM as they suffer from the assumption of Gaussian noise statistics and linearization errors, which can degrade the performance. H∞ filter is one of the alternative of Kalman filter. This paper investigates three SLAM algorithms: (i) EKF SLAM (ii) extended H∞(EH∞) SLAM and (iii) mixed extended H2/H∞(EH2/H∞) SLAM. A comparison of the three algorithms is given through numerical simulations.

AB - The process of simultaneously building the map and locating a vehicle is known as Simultaneous Localization and Mapping (SLAM) and can be used for autonomous navigation. The estimation of vehicle states and landmarks plays an important role in SLAM. Most of the SLAM algorithms are based on extended Kalman filters (EKFs). However, EKF's are not the best choice for SLAM as they suffer from the assumption of Gaussian noise statistics and linearization errors, which can degrade the performance. H∞ filter is one of the alternative of Kalman filter. This paper investigates three SLAM algorithms: (i) EKF SLAM (ii) extended H∞(EH∞) SLAM and (iii) mixed extended H2/H∞(EH2/H∞) SLAM. A comparison of the three algorithms is given through numerical simulations.

U2 - 10.1109/ISIC.2010.5612907

DO - 10.1109/ISIC.2010.5612907

M3 - Chapter

SN - 978-1424453603

SP - 818

EP - 823

BT - 2010 IEEE International Symposium on Intelligent Control

PB - IEEE

CY - Piscataway, NJ

ER -

SLAM using EKF, EH∞ and mixed EH2/H∞ filter

Abstract

Access to Document

Fingerprint

Cite this