Visual Loop Detection in Underwater Robotics: An Unsupervised Deep Learning Approach

Authors Antoni Burguera Burguera | Francisco Bonin-Font
In Proceedings of the IFAC World Congress, Berlin - Virtual, 2020.


This paper presents a novel Deep Neural Network aimed at fast and robust visual loop detection targeted to underwater images. In order to help the proposed network to learn the features that define loop closings, a global image descriptor built upon clusters of local SIFT descriptors is proposed. Also, a method allowing unsupervised training is presented, eliminating the need for a hand-labelled ground truth. Once trained, the Neural Network builds two descriptors of an image that can be easily compared to other image descriptors to ascertain if they close a loop or not. The experimental results, performed using real data gathered in coastal areas of Mallorca (Spain), show the validity of our proposal and favorably compares it to previously existing methods.

Uso de cookies

Este sitio web utiliza cookies para que usted tenga la mejor experiencia de usuario. Si continúa navegando está dando su consentimiento para la aceptación de las mencionadas cookies y la aceptación de nuestra política de cookies, pinche el enlace para mayor información.

ACEPTAR
Aviso de cookies