Work

Deep learning-based robust optical localization for hypersonic platforms

Year: 2026

Type: article

Abstract: 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.

Source: CEAS Space Journal

DOI: 10.1007/s12567-026-00724-0

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

Institutions: Université Paris-Saclay, Office National d'Études et de Recherches Aérospatiales, Département Traitement de l’Information et Systèmes

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funder: Office National d'études et de Recherches Aérospatiales