A Collaborative Graph-based SLAM Framework Using a Computationally Effective Measurement Algebra

Authors

  • Gábor Péter
    Affiliation
    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
  • Bálint Kiss
    Affiliation
    Department of Control Engineering and Information Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
https://doi.org/10.3311/PPee.21358

Abstract

Simultaneous localization and mapping (SLAM) is an essential task for autonomous rover navigation in an unknown environment, especially if no absolute location information is available. This paper presents a computationally lightweight framework to enable agents with limited processing power to carry out the SLAM cooperatively and without absolute onboard localization sensors in a 2D environment. The proposed solution is built on a graph-based map representation, where nodes (resp. edges) represent landmarks (resp. odometry-based relative measurements), a measurement algebra with embedded uncertainty, and a compact database format that could be stored on a server in a centralized manner. The operations required by the agents to insert a new landmark in the graph, update landmark positions and combine measurements as a loop is closed in the graph are detailed. The resulting framework was tested in a laboratory environment and on a public dataset with encouraging results; hence our method can be used for cost-effective indoor mobile agents with limited computational resources and onboard sensors to achieve a mapping while keeping track of the agent's position. The method can also be easily generalized for a 3D scenario.

Keywords:

SLAM, lightweight, algebra, graph, uncertainty

Citation data from Crossref and Scopus

Published Online

2023-10-10

How to Cite

Péter, G., Kiss, B. “A Collaborative Graph-based SLAM Framework Using a Computationally Effective Measurement Algebra”, Periodica Polytechnica Electrical Engineering and Computer Science, 67(4), pp. 403–412, 2023. https://doi.org/10.3311/PPee.21358

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Articles