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Localization in Dynamic Planar Environments Using Few Distance Measurements

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arxiv 2407.03219 v2 pith:CWGBULZG submitted 2024-07-03 cs.RO

Michael M. Bilevich , Shahar Guini , Dan Halperin This is my paper · ORCID
classification cs.RO
keywords dynamicunknowndensitydistanceenvironmentenvironmentsknownlocalization
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We present a method for determining the unknown location of a sensor placed in a known 2D environment in the presence of unknown dynamic obstacles, using only few distance measurements. We present guarantees on the quality of the localization, which are robust under mild assumptions on the density of the unknown/dynamic obstacles in the known environment. We demonstrate the effectiveness of our method in simulated experiments for different environments and varying dynamic-obstacle density. Our open source software is available at https://github.com/TAU-CGL/vb-fdml2-public.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Lifelong Localization in Dynamic Indoor Environments Combining Odometry with Sparse Distance Sampling

    cs.RO 2026-07 conditional novelty 4.0 of 10

    A lifelong indoor localization framework fuses odometry with sparse distance sampling and provably retains a pose close to ground truth, provided the dynamic environment is correctly characterized.

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