Underwater Navigation and Mapping with an Omnidirecional Optical Sensor
Underwater Navigation and Mapping with an Omnidirecional Optical Sensor

Author: Josep Bosch Alay

Publisher:

Published: 2018

Total Pages: 120

ISBN-13:

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Omnidirectional vision has received increasing interest during the last decade from the computer vision community. However, the use of omnidirectional cameras underwater is still very limited. In this thesis we propose several methods to create a reference resource for designing, calibrating and using underwater omnidirectional multi-camera systems (OMS). The first problem we address is their design and calibration. Next, we study stitching strategies to generate omnidirectional panoramas from individual images. Finally, we focus on potential underwater applications. We first explore the promising uses of omnidirectional cameras to create immersive virtual experiences and secondly, we demonstrate the capabilities of omnidirectional cameras as complementary sensors for the navigation of underwater robots. To validate all presented algorithms, two custom omnidirectional cameras were built and several experiments with divers and underwater robots have been carried out to collect the necessary data.

Autonomous Underwater Vehicle Navigation and Mapping in Dynamic, Unstructured Environments
Autonomous Underwater Vehicle Navigation and Mapping in Dynamic, Unstructured Environments

Author: Clayton Gregory Kunz

Publisher:

Published: 2012

Total Pages: 98

ISBN-13:

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This thesis presents a system for automatically building 3-D optical and bathymetric maps of underwater terrain using autonomous robots. The maps that are built improve the state of the art in resolution by an order of magnitude, while fusing bathymetric information from acoustic ranging sensors with visual texture captured by cameras. As part of the mapping process, several internal relationships between sensors are automatically calibrated, including the roll and pitch offsets of the velocity sensor, the attitude offset of the multibeam acoustic ranging sensor, and the full six-degree of freedom offset of the camera. The system uses pose graph optimization to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame, and takes into account the case where the terrain being mapped is drifting and rotating by estimating the orientation of the terrain at each time step in the robot's trajectory. Relative pose constraints are introduced into the pose graph based on multibeam submap matching using depth image correlation, while landmark-based constraints are used in the graph where visual features are available. The two types of constraints work in concert in a single optimization, fusing information from both types of mapping sensors and yielding a texture-mapped 3-D mesh for visualization. The optimization framework also allows for the straightforward introduction of constraints provided by the particular suite of sensors available, so that the navigation and mapping system presented works under a variety of deployment scenarios, including the potential incorporation of external localization systems such as long-baseline acoustic networks. Results of using the system to map the draft of rotating Antarctic ice floes are presented, as are results fusing optical and range data of a coral reef.

A High-Rate Virtual Instrument of Marine Vehicle Motions for Underwater Navigation and Ocean Remote Sensing
A High-Rate Virtual Instrument of Marine Vehicle Motions for Underwater Navigation and Ocean Remote Sensing

Author: Chrystel Gelin

Publisher: Springer Science & Business Media

Published: 2012-08-22

Total Pages: 113

ISBN-13: 3642320155

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Dead-Reckoning aided with Doppler velocity measurement has been the most common method for underwater navigation for small vehicles. Unfortunately DR requires frequent position recalibrations and underwater vehicle navigation systems are limited to periodic position update when they surface. Finally standard Global Positioning System (GPS) receivers are unable to provide the rate or precision required when used on a small vessel. To overcome this, a low cost high rate motion measurement system for an Unmanned Surface Vehicle (USV) with underwater and oceanographic purposes is proposed. The proposed onboard system for the USV consists of an Inertial Measurement Unit (IMU) with accelerometers and rate gyros, a GPS receiver, a flux-gate compass, a roll and tilt sensor and an ADCP. Interfacing all the sensors proved rather challenging because of their different characteristics. The proposed data fusion technique integrates the sensors and develops an embeddable software package, using real time data fusion methods, for a USV to aid in navigation and control as well as controlling an onboard Acoustic Doppler Current Profiler (ADCP). While ADCPs non-intrusively measure water flow, the vessel motion needs to be removed to analyze the data and the system developed provides the motion measurements and processing to accomplish this task.

Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles
Development and Testing of Navigation Algorithms for Autonomous Underwater Vehicles

Author: Francesco Fanelli

Publisher: Springer

Published: 2019-04-16

Total Pages: 108

ISBN-13: 303015596X

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This book focuses on pose estimation algorithms for Autonomous Underwater Vehicles (AUVs). After introducing readers to the state of the art, it describes a joint endeavor involving attitude and position estimation, and details the development of a nonlinear attitude observer that employs inertial and magnetic field data and is suitable for underwater use. In turn, it shows how the estimated attitude constitutes an essential type of input for UKF-based position estimators that combine position, depth, and velocity measurements. The book discusses the possibility of including real-time estimates of sea currents in the developed estimators, and highlights simulations that combine real-world navigation data and experimental test campaigns to evaluate the performance of the resulting solutions. In addition to proposing novel algorithms for estimating the attitudes and positions of AUVs using low-cost sensors and taking into account magnetic disturbances and ocean currents, the book provides readers with extensive information and a source of inspiration for the further development and testing of navigation algorithms for AUVs.

Autonomous Underwater Vehicle Guidance, Navigation, and Control
Autonomous Underwater Vehicle Guidance, Navigation, and Control

Author: Timothy Sands

Publisher:

Published: 2020

Total Pages: 0

ISBN-13:

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A considerable volume of research has recently blossomed in the literature on autonomous underwater vehicles accepting recent developments in mathematical modeling and system identification; pitch control; information filtering and active sensing, including inductive sensors of ELF emissions and also optical sensor arrays for position, velocity, and orientation detection; grid navigation algorithms; and dynamic obstacle avoidance among others. In light of these modern developments, this article develops and compares integrative guidance, navigation, and control methodologies for the Naval Postgraduate School,Äôs Phoenix, a submerged autonomous vehicle. The measure of merit reveals how well each of several methodologies cope with known and unknown disturbance currents that can be constant or harmonic while maintaining safe passage distance from underwater obstacles, in this case submerged mines.

ROBOT 2017: Third Iberian Robotics Conference
ROBOT 2017: Third Iberian Robotics Conference

Author: Anibal Ollero

Publisher: Springer

Published: 2017-11-10

Total Pages: 913

ISBN-13: 3319708333

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These volumes of "Advances in Intelligent Systems and Computing" highlight papers presented at the "Third Iberian Robotics Conference (ROBOT 2017)". Held from 22 to 24 November 2017 in Seville, Spain, the conference is a part of a series of conferences co-organized by SEIDROB (Spanish Society for Research and Development in Robotics) and SPR (Portuguese Society for Robotics). The conference is focused on Robotics scientific and technological activities in the Iberian Peninsula, although open to research and delegates from other countries. Thus, it has more than 500 authors from 21 countries. The volumes present scientific advances but also robotic industrial applications, looking to promote new collaborations between industry and academia.

Toward Lifelong Visual Localization and Mapping
Toward Lifelong Visual Localization and Mapping

Author: Hordur Johannsson

Publisher:

Published: 2013

Total Pages: 181

ISBN-13:

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Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications. Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with a BlueView forward-looking sonar. The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an extended period of time.

Optical Proximity Sensor and Orientation Control of Autonomous, Underwater Robot
Optical Proximity Sensor and Orientation Control of Autonomous, Underwater Robot

Author: Martin Lozano (Jr.)

Publisher:

Published: 2014

Total Pages: 92

ISBN-13:

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Autonomous mobile robots need a reliable means of navigation to reach their target while avoiding collisions. This requires continuous knowledge of the vehicle's position, orientation, and motion as well as a way to identify their surroundings. Exploratory robots and those traveling in complex environments may have difficulty determining their global location. They often rely on data from sensors to estimate their position. While various proximity sensors have been developed for land vehicles, options for underwater vehicles are limited. We detail the design of an optical orientation sensor for fine positioning of highly maneuverable underwater robots. The sensor consists of a camera-laser system (CLS) to geometrically estimate distances to points on a surface. By aggregating and analyzing several data points from multiple lasers, an estimate of the robot's distance, yaw, and pitch are determined. A prototype sensor is constructed and shown to achieve highly accurate distance estimates ( 1mm) at close ranges within 270mm and yaw rotation estimates of 2* within the range of 30*. We also show the successful integration of a gyro with the CLS on an autonomous surface vehicle. The fused estimate of the two sensors results in improved dynamic performance than either sensor alone. The optical sensor corrects the unbounded position error of the gyro measurements with the added benefit of external feedback to avoid collisions in dynamic environments. The gyro provides high frequency orientation estimation in between optical measurements, greatly reduces transient behavior, and generally smoothens vehicle motion. Using this sensor, an underwater robot exploring a complex environment can estimate its orientation relative to a surface in real-time, allowing the robot to avoid collisions with the sensitive environment or maintain a desired orientation while autonomously tracking objects of interest.