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<p>Sensor-based localization is required for long-range platforms when GNSS can be denied. To bypass the structural limitations of the classical registration-on-reference-image framework, we offer in this paper to encode the appearance of the surrounding of the target (at all resolutions) from a stack of images of the scene into a deep network. This new framework outperforms the registration baseline in our experiments, in particular on a bimodal scene (which can or can not be snowy). This invites larger benchmarks from academic and industrial community to conclude on the applicability of this method on real use cases.</p>

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Deep learning-based robust optical localization for hypersonic platforms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

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  • Adrien Chan-Hon-Tong,
  • Aurelien Plyer,
  • Baptiste Cadalen,
  • Laurent Serre

摘要

Sensor-based localization is required for long-range platforms when GNSS can be denied. To bypass the structural limitations of the classical registration-on-reference-image framework, we offer in this paper to encode the appearance of the surrounding of the target (at all resolutions) from a stack of images of the scene into a deep network. This new framework outperforms the registration baseline in our experiments, in particular on a bimodal scene (which can or can not be snowy). This invites larger benchmarks from academic and industrial community to conclude on the applicability of this method on real use cases.