This paper proposes the posterior linearization backward simultaneous localization and mapping (PLB-SLAM) algorithm for batch SLAM problems. Based on motion and landmark measurements, we aim to estimate the trajectory of… Click to show full abstract
This paper proposes the posterior linearization backward simultaneous localization 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-Blackwellized 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 linearization to obtain Gaussian approximations of the distribution of the landmarks. PLB-SLAM is assessed via numerical simulations and real experiments for indoor localization and mapping of radio beacons using a smartphone, Bluetooth beacons, and Wi-Fi access points.
               
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