Print Email Facebook Twitter Rao-Blackwellized particle smoothing for simultaneous localization and mapping Title Rao-Blackwellized particle smoothing for simultaneous localization and mapping Author Kok, M. (TU Delft Team Manon Kok) Solin, Arno (Aalto University) Schön, Thomas B. (Uppsala University) Date 2024 Abstract Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic interpretation of the SLAM task allows for incorporating prior knowledge and for operation under uncertainty. Contrary to the common practice of computing point estimates of the system states, we capture the full posterior density through approximate Bayesian inference. This dynamic learning task falls under state estimation, where the state-of-the-art is in sequential Monte Carlo methods that tackle the forward filtering problem. In this paper, we introduce a framework for probabilistic SLAM using particle smoothing that does not only incorporate observed data in current state estimates, but it also backtracks the updated knowledge to correct for past drift and ambiguities in both the map and in the states. Our solution can efficiently handle both dense and sparse map representations by Rao-Blackwellization of conditionally linear and conditionally linearized models. We show through simulations and real-world experiments how the principles apply to radio (Bluetooth low-energy/Wi-Fi), magnetic field, and visual SLAM. The proposed solution is general, efficient, and works well under confounding noise. Subject Gaussian processesmagnetic field localizationparticle smoothingsequential Monte Carlosimultaneous localization and mapping To reference this document use: http://resolver.tudelft.nl/uuid:b8b3b7a4-ef80-4737-826b-686ece650aa6 DOI https://doi.org/10.1017/dce.2024.12 Source Data-Centric Engineering, 5 (4) Part of collection Institutional Repository Document type journal article Rights © 2024 M. Kok, Arno Solin, Thomas B. Schön Files PDF rao-blackwellized-particl ... apping.pdf 1.41 MB Close viewer /islandora/object/uuid:b8b3b7a4-ef80-4737-826b-686ece650aa6/datastream/OBJ/view