Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Bernardo Teixeira (Author) Other; Hugo Silva (Author) Other; Aníbal Matos (Author) FEUP; Eduardo Silva (Author) Other

Conference proceedings International

Title: OCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

Pages: 1-9

2019 OCEANS MTS/IEEE Seattle, OCEANS 2019

27 October 2019 through 31 October 2019

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 4 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9