Deep Learning for Underwater Visual Odometry Estimation

Abstract

This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.

An Integrated Visual Odometry System With Stereo Camera for Unmanned Underwater Vehicles

“…The depths of feature points are derived by triangulation calculation. Teixeira [40] investigated implementation of a deep learning algorithm on underwater visual odometry estimation. However, such an algorithm requires a large volume of training data and it is not robust.…”

“…Compared with the nonlinear optimisation algorithms in other VOs, the IMU's measurement is used to constrain the rotation vector and the translation vector estimated by the pure visual odometry. In equation (40), the IMU constraint is introduced by the probabilistic model. In this case, the effects of the imaging noise can be decreased.…”

Simultaneous Localization and Mapping for Inspection Robots in Water and Sewer Pipe Networks: A Review

“…Another important class of VO method is based on deep learning, using convolutional neural networks [96], deep recurrent convolutional neural networks (DeepVO/ESP-VO) [97], [98], unsupervised deep learning (UnDeepVO) [28], generative adversarial networks [99] and deep networks driven by optic flow [100]- [102]. In [103] a deep learning VO method was developed for underwater applications, which is relevant to water distribution pipes, and showed promise compared to standard methods (although the underwater environment did prove challenging for pose estimation). Full vSLAM has also been addressed using deep learning [104].…”

DeepQGHO: Quantized Greedy Hyperparameter Optimization in Deep Neural Networks for on-the-Fly Learning

“…Deep Neural Networks (DNNs) have stretched the boundaries of Artificial Intelligence (AI) across a variety of tasks, including object recognition from images [3] [4], machine vision [5], natural language processing [6], and speech recognition [7]. They have been used in real-world applications such as estimation of driving energy for planetary rovers [8], SAR target recognition, terrain classification [9], underwater visual odometry estimation [10], interactive medical image segmentation [11], and self-driving vehicles [12]. In this study, we focus on greedy approach based hyper-parameter optimization for on-the-fly training applications in deep neural networks.…”