1,220 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
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
AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization
We present a new dataset, dedicated to the development of simultaneous
localization and mapping methods for underwater vehicles navigating close to
the seabed. The data sequences composing this dataset are recorded in three
different environments: a harbor at a depth of a few meters, a first
archaeological site at a depth of 270 meters and a second site at a depth of
380 meters. The data acquisition is performed using Remotely Operated Vehicles
equipped with a monocular monochromatic camera, a low-cost inertial measurement
unit, a pressure sensor and a computing unit, all embedded in a single
enclosure. The sensors' measurements are recorded synchronously on the
computing unit and seventeen sequences have been created from all the acquired
data. These sequences are made available in the form of ROS bags and as raw
data. For each sequence, a trajectory has also been computed offline using a
Structure-from-Motion library in order to allow the comparison with real-time
localization methods. With the release of this dataset, we wish to provide data
difficult to acquire and to encourage the development of vision-based
localization methods dedicated to the underwater environment. The dataset can
be downloaded from: http://www.lirmm.fr/aqualoc/Comment: The International Journal of Robotics Research, SAGE Publications,
201
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
High Definition, Inexpensive, Underwater Mapping
In this paper we present a complete framework for Underwater SLAM utilizing a
single inexpensive sensor. Over the recent years, imaging technology of action
cameras is producing stunning results even under the challenging conditions of
the underwater domain. The GoPro 9 camera provides high definition video in
synchronization with an Inertial Measurement Unit (IMU) data stream encoded in
a single mp4 file. The visual inertial SLAM framework is augmented to adjust
the map after each loop closure. Data collected at an artificial wreck of the
coast of South Carolina and in caverns and caves in Florida demonstrate the
robustness of the proposed approach in a variety of conditions.Comment: IEEE Internation Conference on Robotics and Automation, 202
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