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Hypothesis selection with Monte Carlo tree search for feature-based simultaneous localization and mapping in non-static environments
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Epiroc Rock Drills AB, Sweden; Epiroc Rock Drills AB, Sweden.ORCID iD: 0000-0003-1137-9282
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1971-4295
2024 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176, Vol. 43, no 6, p. 750-764Article in journal (Refereed) Published
Abstract [en]

A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not a valid assumption. This paper studies a scenario where landmarks can occupy multiple discrete positions at different points in time, where each possible position is added to a multi-hypothesis map representation. A selector-mixture distribution is introduced and used in the observation model. Each landmark position hypothesis is associated with one component in the mixture. The landmark movements are modeled by a discrete Markov chain and the Monte Carlo tree search algorithm is suggested to be used as component selector. The non-static environment model is further incorporated into the factor graph formulation of the SLAM problem and is solved by iterating between estimating discrete variables with a component selector and optimizing continuous variables with an efficient state-of-the-art nonlinear least squares SLAM solver. The proposed non-static SLAM system is validated in numerical simulation and with a publicly available dataset by showing that a non-static environment can successfully be navigated.

Place, publisher, year, edition, pages
SAGE PUBLICATIONS LTD , 2024. Vol. 43, no 6, p. 750-764
Keywords [en]
Monte Carlo tree search; non-static environment; simultaneous localization and mapping; multi-hypothesis, WASP_publications
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-199550DOI: 10.1177/02783649231215095ISI: 001104548700001OAI: oai:DiVA.org:liu-199550DiVA, id: diva2:1818535
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2024-10-28Bibliographically approved

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Nielsen, KristinHendeby, Gustaf

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