| Autor: |
Amit Efraim, Joseph M. Francos |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Advances in Signal Processing, Vol 2024, Iss 1, Pp 1-25 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1687-6180 |
| DOI: |
10.1186/s13634-024-01153-z |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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