Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Bernardo Teixeira (Autor) Outra; Hugo Silva (Autor) Outra; Aníbal Matos (Autor) FEUP; Eduardo Silva (Autor) Outra

Ata de Conferência Internacional

Título: OCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

Páginas: 1-9

2019 OCEANS MTS/IEEE Seattle, OCEANS 2019

27 October 2019 through 31 October 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 4 Citações

Outras Informações

ID Authenticus: P-00R-P5M

DOI: 10.23919/oceans40490.2019.8962872

Abstract (EN): This paper address the use of deep learning approaches for visual based navigation in confined underwater environments. State-of-the-art algorithms have shown the tremendous potential deep learning architectures can have for visual navigation implementations, though they are still mostly outperformed by classical feature-based techniques. In this work, we apply current state-of-the-art deep learning methods for visual-based robot navigation to the more challenging underwater environment, providing both an underwater visual dataset acquired in real operational mission scenarios and an assessment of state-of-the-art algorithms on the underwater context. We extend current work by proposing a novel pose optimization architecture for the purpose of correcting visual odometry estimate drift using a Visual-Inertial fusion network, consisted of a neural network architecture anchored on an Inertial supervision learning scheme. Our Visual-Inertial Fusion Network was shown to improve results an average of 50% for trajectory estimates, also producing more visually consistent trajectory estimates for both our underwater application scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9