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Piecewise-deterministic Quasi-static Pose Graph SLAM in Unstructured Dynamic Environments

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Abstract

This research is inspired by the challenge of providing situational awareness to a ship looking for leads in the ice to navigate the Canadian Arctic. To address this challenge, this paper proposes piecewise-deterministic quasi-static simultaneous localization and mapping (PDQS-SLAM). PDQS-SLAM relaxes the static assumption that is inherent to traditional SLAM algorithms by incorporating landmark kinematic motion models in the graph construction. It then generalizes to more complex dynamic environments by detecting inevitable disruptions to the landmark’s motion model. This is achieved by augmenting the definition of a loop closure factor to include a state, governed by a finite state machine, that captures the edge’s behaviour over time. Simulations and laboratory experiments demonstrate that the localization and mapping errors of PDQS-SLAM in a dynamic environment with mobile landmarks that experience changes in their motion model were similar to the baseline static SLAM in a static environment. This represents a capability that is needed for persistent autonomous operations in complex environments populated with slowly-evolving landmarks, such as the Canadian marine Arctic.

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The data and code are not available in a public repository.

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Acknowledgements

The support of the Natural Sciences and Engineering Research Council (NSERC), Nova Scotia Graduate Scholarship (NSGS), and Irving Shipbuilding Chair in Marine Engineering and Autonomous Systems, is gratefully acknowledged.

Funding

Funding for this research has been provided by grants from the Natural Sciences and Engineering Research Council (NSERC), Nova Scotia Graduate Scholarship (NSGS), and Irving Shipbuilding Chair in Marine Engineering and Autonomous Systems.

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Authors and Affiliations

  1. Mechanical Engineering, Dalhousie University, 5217 Morris Street, 4th floor, P.O. Box 15000, Halifax, B3H 4R2, Nova Scotia, Canada

    Amy Deeb, Mae Seto & Ya-Jun Pan

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  1. Amy Deeb
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  2. Mae Seto
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  3. Ya-Jun Pan
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Contributions

This article presents a portion of Dr. A. Deeb’s doctoral thesis, which was co-supervised equally by Dr. M. L. Seto and Dr. Y.-J. Pan. All authors made substantial contributions to the conception or design of the work, the analysis and interpretation of the data, and the draft of the manuscript.

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Correspondence to Amy Deeb.

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The authors have no competing interests to declare that are relevant to the content of this article.

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Mae L. Seto and Ya-Jun Pan contributed equally to this work.

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Deeb, A., Seto, M. & Pan, YJ. Piecewise-deterministic Quasi-static Pose Graph SLAM in Unstructured Dynamic Environments. J Intell Robot Syst 106, 34 (2022). https://doi.org/10.1007/s10846-022-01739-5

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