882 research outputs found
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
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
3D virtualization of an underground semi-submerged cave system
Underwater caves represent the most challenging scenario for exploration, mapping and 3D modelling. In such complex environment, unsuitable to humans, highly specialized skills and expensive equipment are normally required. Technological progress and scientific innovation attempt, nowadays, to develop safer and more automatic approaches for the virtualization of these complex and not easily accessible environments, which constitute a unique natural, biological and cultural heritage. This paper presents a pilot study realised for the virtualization of 'Grotta Giusti' (Fig. 1), an underground semi-submerged cave system in central Italy. After an introduction on the virtualization process in the cultural heritage domain and a review of techniques and experiences for the virtualization of underground and submerged environments, the paper will focus on the employed virtualization techniques. In particular, the developed approach to
simultaneously survey the semi-submersed areas of the cave relying on a stereo camera system and the virtualization of the virtual cave will be discussed
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
CaveSeg: Deep Semantic Segmentation and Scene Parsing for Autonomous Underwater Cave Exploration
In this paper, we present CaveSeg - the first visual learning pipeline for
semantic segmentation and scene parsing for AUV navigation inside underwater
caves. We address the problem of scarce annotated training data by preparing a
comprehensive dataset for semantic segmentation of underwater cave scenes. It
contains pixel annotations for important navigation markers (e.g. caveline,
arrows), obstacles (e.g. ground plain and overhead layers), scuba divers, and
open areas for servoing. Through comprehensive benchmark analyses on cave
systems in USA, Mexico, and Spain locations, we demonstrate that robust deep
visual models can be developed based on CaveSeg for fast semantic scene parsing
of underwater cave environments. In particular, we formulate a novel
transformer-based model that is computationally light and offers near real-time
execution in addition to achieving state-of-the-art performance. Finally, we
explore the design choices and implications of semantic segmentation for visual
servoing by AUVs inside underwater caves. The proposed model and benchmark
dataset open up promising opportunities for future research in autonomous
underwater cave exploration and mapping.Comment: submitted for review in ICRA 2024. 10 pages, 9 figure
Weakly Supervised Caveline Detection For AUV Navigation Inside Underwater Caves
Underwater caves are challenging environments that are crucial for water
resource management, and for our understanding of hydro-geology and history.
Mapping underwater caves is a time-consuming, labor-intensive, and hazardous
operation. For autonomous cave mapping by underwater robots, the major
challenge lies in vision-based estimation in the complete absence of ambient
light, which results in constantly moving shadows due to the motion of the
camera-light setup. Thus, detecting and following the caveline as navigation
guidance is paramount for robots in autonomous cave mapping missions. In this
paper, we present a computationally light caveline detection model based on a
novel Vision Transformer (ViT)-based learning pipeline. We address the problem
of scarce annotated training data by a weakly supervised formulation where the
learning is reinforced through a series of noisy predictions from intermediate
sub-optimal models. We validate the utility and effectiveness of such weak
supervision for caveline detection and tracking in three different cave
locations: USA, Mexico, and Spain. Experimental results demonstrate that our
proposed model, CL-ViT, balances the robustness-efficiency trade-off, ensuring
good generalization performance while offering 10+ FPS on single-board (Jetson
TX2) devices
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