In this study, a novel filtering method called Randomized Sigma Point Kalman Filter (RSPKF) is introduced for feature based 3D Simultaneous Localization and Mapping (SLAM). Conventional SLAM methods are mostly based on Extended Kalman Filters (EKF) for ‘mild’ nonlinear processes and Unscented KF (UKF) or Cubature KF (CKF) for ‘aggressive’ nonlinear processes. A critical problem of the existing filtering methods is that they lead to biased estimates of the state and measurement statistics. The main purpose of this study is to propose a new local filter, RSPKF, based on stochastic integration rules providing an unbiased estimate of an integral for feature based SLAM. The simulation based on point features in 2D and experimental results based on planar features in 3D show that the RSPKF based SLAM method provides more accurate results than the traditional methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Planar-Feature Based 3D SLAM Using Randomized Sigma Point Kalman Filters


    Contributors:

    Conference:

    Itzhack Y. Bar-Itzhack Memorial Symposium on Estimation, Navigation, and Spacecraft Control ; 2012 ; Haifa, Israel October 14, 2012 - October 17, 2017



    Publication date :

    2015-01-01


    Size :

    16 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Sigma Point Kalman Filters for Efficient Orbit Estimation (AAS 03-525)

    Lee, D.-J. / Alfriend, K. T. / American Astronautical Society | British Library Conference Proceedings | 2004


    A comparison of sigma-point Kalman filters on an aerospace actuator

    Al-Shabi, Mohammad / Gadsden, S. Andrew / El Haj Assad, Mamdouh et al. | SPIE | 2021


    Cubature Kalman Filter based point set registration for SLAM

    Liang Li / Ming Yang / Chunxiang Wang et al. | IEEE | 2016



    Plane-feature based 3D outdoor SLAM with Gaussian filters

    Ulas, Cihan / Temeltas, Hakan | IEEE | 2012