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A Multi-Sensor Fusion-Based Underwat...

Rahman, Sharmin.

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A Multi-Sensor Fusion-Based Underwater Slam System.

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Multi-Sensor Fusion-Based Underwater Slam System./
Author:	Rahman, Sharmin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	118 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28024866
ISBN:	9798678107886

A Multi-Sensor Fusion-Based Underwater Slam System.
Rahman, Sharmin.

A Multi-Sensor Fusion-Based Underwater Slam System. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 118 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--University of South Carolina, 2020.

This item must not be sold to any third party vendors.

This dissertation addresses the problem of real-time Simultaneous Localization and Mapping (SLAM) in challenging environments. SLAM is one of the key enabling technologies for autonomous robots to navigate in unknown environments by processing information on their onboard computational units. In particular, we study the exploration of challenging GPS denied underwater environments to enable a wide range of robotic applications, ranging from historical studies to health monitoring of coral reef; underwater infrastructure inspection e.g., bridges, hydroelectric dams, water supply systems and oil rigs. Mapping underwater structures is important in several fields, such as, marine archaeology, Search and Rescue (SaR), resource management, hydrogeology, and speleology. However, due to the highly unstructured nature of such environments, navigation by human divers could be extremely dangerous, tedious and labor intensive. Hence, employing an underwater robot is an excellent fit to build the map of the environment while simultaneously localizing itself in the map. The main contribution of this dissertation is the design and development of a real-time robust SLAM algorithm for small and large scale underwater environments. SVIn - a novel tightly-coupled keyframe-based non-linear optimization framework fusing Sonar, Visual, Inertial and water depth information with robust initialization, loop-closing, and relocalization capabilities has been presented. Introducing acoustic range information to aid the visual data in underwater, shows improved reconstruction and localization. The availability of depth information from water pressure enables a robust initialization and refines the scale; as well as assists to reduce the drift for the tightly-coupled integration. The complementary characteristics of these sensing modalities provide accurate, and robust localization in unstructured environments with low visibility, and low visual features - as such make them the ideal choice for underwater navigation. The developed software has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world scenarios, enabling many robotic tasks e.g., planning for underwater robot in presence of obstacles. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle (AUV) Aqua2 from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness. To aid the sparse reconstruction, a contour-based reconstruction approach utilizing the well defined edges between the well lit areas, and darkness has been developed. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes these contours to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental result in an underwater cave demonstrate the performance of our system. This enables more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions.

ISBN: 9798678107886Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Computer vision

A Multi-Sensor Fusion-Based Underwater Slam System.
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245 1 0 $a A Multi-Sensor Fusion-Based Underwater Slam System.
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300 $a 118 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
500 $a Advisor: Rekleitis, Ioannis.
502 $a Thesis (Ph.D.)--University of South Carolina, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation addresses the problem of real-time Simultaneous Localization and Mapping (SLAM) in challenging environments. SLAM is one of the key enabling technologies for autonomous robots to navigate in unknown environments by processing information on their onboard computational units. In particular, we study the exploration of challenging GPS denied underwater environments to enable a wide range of robotic applications, ranging from historical studies to health monitoring of coral reef; underwater infrastructure inspection e.g., bridges, hydroelectric dams, water supply systems and oil rigs. Mapping underwater structures is important in several fields, such as, marine archaeology, Search and Rescue (SaR), resource management, hydrogeology, and speleology. However, due to the highly unstructured nature of such environments, navigation by human divers could be extremely dangerous, tedious and labor intensive. Hence, employing an underwater robot is an excellent fit to build the map of the environment while simultaneously localizing itself in the map. The main contribution of this dissertation is the design and development of a real-time robust SLAM algorithm for small and large scale underwater environments. SVIn - a novel tightly-coupled keyframe-based non-linear optimization framework fusing Sonar, Visual, Inertial and water depth information with robust initialization, loop-closing, and relocalization capabilities has been presented. Introducing acoustic range information to aid the visual data in underwater, shows improved reconstruction and localization. The availability of depth information from water pressure enables a robust initialization and refines the scale; as well as assists to reduce the drift for the tightly-coupled integration. The complementary characteristics of these sensing modalities provide accurate, and robust localization in unstructured environments with low visibility, and low visual features - as such make them the ideal choice for underwater navigation. The developed software has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world scenarios, enabling many robotic tasks e.g., planning for underwater robot in presence of obstacles. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle (AUV) Aqua2 from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness. To aid the sparse reconstruction, a contour-based reconstruction approach utilizing the well defined edges between the well lit areas, and darkness has been developed. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures, and the shadow contours. The proposed method utilizes these contours to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental result in an underwater cave demonstrate the performance of our system. This enables more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions.
590 $a School code: 0202.
650 4 $a Robotics. $3 519753
650 4 $a Geographic information science. $3 3432445
650 4 $a Ocean engineering. $3 660731
650 4 $a Artificial intelligence. $3 516317
653 $a Computer vision
653 $a Robotics
653 $a Sensor fusion
653 $a Simultaneous localization and mapping
653 $a Autonomous robots
653 $a GPS
653 $a Underwater environments
653 $a Underwater infrastructure
690 $a 0771
690 $a 0800
690 $a 0547
690 $a 0370
710 2 $a University of South Carolina. $b Computer Science & Engineering. $3 1024028
773 0 $t Dissertations Abstracts International $g 82-04B.
790 $a 0202
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28024866