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Robust point cloud registration for map-based autonomous robot navigation

Efraim, Amit ; Francos, Joseph M.

EURASIP journal on advances in signal processing, 2024-12, Vol.2024 (1), p.57-25 [Periódico revisado por pares]

Cham: Springer International Publishing

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  • Título:
    Robust point cloud registration for map-based autonomous robot navigation
  • Autor: Efraim, Amit ; Francos, Joseph M.
  • Assuntos: Algorithms ; Autonomous navigation ; Electronics in navigation ; Engineering ; Hypotheses ; Image registration ; Navigation ; Navigation systems ; Place recognition ; Point clouds ; Quantum Information Technology ; Registration ; Robots ; Robustness ; Robustness (mathematics) ; Signal,Image and Speech Processing ; Spintronics ; Surface layers ; Three dimensional models
  • É parte de: EURASIP journal on advances in signal processing, 2024-12, Vol.2024 (1), p.57-25
  • Descrição: Autonomous navigation in large-scale and complex environments in the absence of a GPS signal is a fundamental challenge encountered in a variety of applications. Since 3-D scans provide inherent robustness to ambient illumination changes and the type of the surface texture, we present Point Cloud Map-based Navigation (PCMN), a robust robot navigation system, based exclusively on 3-D point cloud registration between an acquired observation and a stored reference map. It provides a drift-free navigation solution, equipped with a failed registration detection capability. The backbone of the navigation system is a robust point cloud registration method, of the acquired observation to the stored reference map. The proposed registration algorithm follows a hypotheses generation and evaluation paradigm, where multiple statistically independent hypotheses are generated from local neighborhoods of putative matching points. Then, hypotheses are evaluated using a multiple consensus analysis that integrates evaluation of the point cloud feature correlation and a consensus test on the Special Euclidean Group SE(3) based on independent hypothesized estimates. The proposed PCMN is shown to achieve significantly better performance than state-of-the-art methods, both in terms of place recognition recall and localization accuracy, achieving submesh resolution accuracy, both for indoor and outdoor settings.
  • Editor: Cham: Springer International Publishing
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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