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 proceedingChapterpeer-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 languageEnglish
    Title of host publication2010 IEEE International Symposium on Intelligent Control
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages818-823
    ISBN (Print)978-1424453603
    DOIs
    Publication statusPublished - 2010

    Fingerprint Dive into the research topics of 'SLAM using EKF, EH∞ and mixed EH2/H∞ filter'. Together they form a unique fingerprint.

    Cite this