Rao-Blackwellized Posterior Linearization Backward SLAM

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.