25 research outputs found
Real-time Model-based Image Color Correction for Underwater Robots
Recently, a new underwater imaging formation model presented that the
coefficients related to the direct and backscatter transmission signals are
dependent on the type of water, camera specifications, water depth, and imaging
range. This paper proposes an underwater color correction method that
integrates this new model on an underwater robot, using information from a
pressure depth sensor for water depth and a visual odometry system for
estimating scene distance. Experiments were performed with and without a color
chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the
performance of our proposed method by comparing it with other statistic-,
physic-, and learning-based color correction methods. Applications for our
proposed method include improved 3D reconstruction and more robust underwater
robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
An Autonomous Surface Vehicle for Long Term Operations
Environmental monitoring of marine environments presents several challenges:
the harshness of the environment, the often remote location, and most
importantly, the vast area it covers. Manual operations are time consuming,
often dangerous, and labor intensive. Operations from oceanographic vessels are
costly and limited to open seas and generally deeper bodies of water. In
addition, with lake, river, and ocean shoreline being a finite resource,
waterfront property presents an ever increasing valued commodity, requiring
exploration and continued monitoring of remote waterways. In order to
efficiently explore and monitor currently known marine environments as well as
reach and explore remote areas of interest, we present a design of an
autonomous surface vehicle (ASV) with the power to cover large areas, the
payload capacity to carry sufficient power and sensor equipment, and enough
fuel to remain on task for extended periods. An analysis of the design and a
discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto
Scalable underwater assembly with reconfigurable visual fiducials
We present a scalable combined localization infrastructure deployment and
task planning algorithm for underwater assembly. Infrastructure is autonomously
modified to suit the needs of manipulation tasks based on an uncertainty model
on the infrastructure's positional accuracy. Our uncertainty model can be
combined with the noise characteristics from multiple devices. For the task
planning problem, we propose a layer-based clustering approach that completes
the manipulation tasks one cluster at a time. We employ movable visual fiducial
markers as infrastructure and an autonomous underwater vehicle (AUV) for
manipulation tasks. The proposed task planning algorithm is computationally
simple, and we implement it on AUV without any offline computation
requirements. Combined hardware experiments and simulations over large datasets
show that the proposed technique is scalable to large areas.Comment: Submitted to ICRA 202
Underwater Exploration and Mapping
This paper analyzes the open challenges of exploring and mapping in the underwater realm with the goal of identifying research opportunities that will enable an Autonomous Underwater Vehicle (AUV) to robustly explore different environments. A taxonomy of environments based on their 3D structure is presented together with an analysis on how that influences the camera placement. The difference between exploration and coverage is presented and how they dictate different motion strategies. Loop closure, while critical for the accuracy of the resulting map, proves to be particularly challenging due to the limited field of view and the sensitivity to viewing direction. Experimental results of enforcing loop closures in underwater caves demonstrate a novel navigation strategy. Dense 3D mapping, both online and offline, as well as other sensor configurations are discussed following the presented taxonomy. Experimental results from field trials illustrate the above analysis.acceptedVersio