Guaranteed SLAM—An interval approach

Mohamed Mustafa, Alexandru Stancu, Nicolas Delanoue, Eduard Codres

    Research output: Contribution to journalArticlepeer-review


    This paper proposes a new approach, interval Simultaneous Localization and Mapping (i-SLAM), which addresses the robotic mapping problem in the context of interval methods, where the robot sensor noise is assumed bounded. With no prior knowledge about the noise distribution or its probability density function, we derive and present necessary conditions to guarantee the map convergence even in the presence of nonlinear observation and motion models. These conditions may require the presence of some anchoring landmarks with known locations. The performance of i-SLAM is compared with the probabilistic counterparts in terms of accuracy and efficiency.
    Original languageEnglish
    Pages (from-to)160-170
    Number of pages11
    JournalRobotics and Autonomous Systems
    Issue numberFebruary 2018
    Early online date2 Dec 2017
    Publication statusPublished - 1 Feb 2018


    • Interval methods
    • Nonlinear models
    • Real analysis
    • SLAM convergence


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