Rao-Blackwellized Posterior Linearization Backward SLAM

Research output: Contribution to journalArticle


Research units

  • University of Liverpool
  • Antonio de Nebrija University


This paper proposes the posterior linearisation backward simultaneous localisation and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of the mobile agent and the landmark positions using an approximate Rao-Blackwellised Monte Carlo solution, as in FastSLAM. PLB-SLAM improves the accuracy of current FastSLAM solutions due to two key aspects: smoothing of the trajectory distribution via backward trajectory simulation and the use of iterated posterior linearisation to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localisation and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points.


Original languageEnglish
Article number8662708
Pages (from-to)4734-4747
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Issue number5
Early online date2019
Publication statusPublished - 1 May 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • Simultaneous localisation and mapping, Back-ward simulation, Posterior linearisation, Rao-Blackwellisation, Bluetooth beacons, Wi-Fi access points, Smartphone, Rao-Blackwellization, Backward simulation, Simultaneous localization and mapping, Posterior linearization

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