Rectilinear coordinate frames for Deep sea navigation

Murphy, Chris; Singh, Hanumant

doi:10.1109/auv.2010.5779654

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…As the earth is not perfectly round, but an ellipsoid, (2.14) represents a simplification. Usually several compensation terms are added to the equation to improve correctness [MS10]. Alternatively, other approaches, such as the one presented by Vincenty [Vin75], can be used.…”

Section: Chapter 2 Probabilistic Sensor Modelsmentioning

confidence: 99%

Smartphone-Based 3D Indoor Localization and Navigation

Ebner¹

2021

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During my master's thesis, which covered the essentials of Wi-Fi location estimation, I came in contact with the concepts of indoor localization, and multi sensor fusion. Having attracted my interest, I hereafter joined the Faculty of Computer Science and Business Information Systems at the University of Applied Sciences Würzburg-Schweinfurt as a research assistant. During that time I conducted the majority of my research on indoor localization and navigation, which finally lead to writing this work.I would like to thank my colleagues during that time, but especially my local doctoral adviser Prof. Dr. Frank Deinzer. Not only did he guide me towards earning a doctors degree, he also provided extensive feedback to all publications, always ensuring utmost scientific quality. I would also like to thank my external doctoral adviser and first referee Prof. Dr. habil. Marcin Grzegorzek for his trust, all provided feedback, and the warm welcome within his research group. I also owe gratitude to my co-workers Lukas, Toni, and Markus. Without hours of mutual feedback, technical input, paired programming, data acquisition, and networking, this work would not have been possible. Likewise, I also wish to thank Prof. Dr. Karsten Huffstadt and Prof. Dr. Arndt Balzer for helping with funding my initial employment.In order to develop a generic indoor localization and navigation solution, the presented system was deployed to and tested within several unique buildings. For this, special thanks go to Dr. Hellmuth Möhring from the RothenburgMuseum in Rothenburg ob der Tauber, Prof. Dr. Günther Moosbauer from the Gäubodenmuseum in Straubing, Angelika Schreiber from the Hutmuseum in Lindenberg, the Landesstelle für die nichtstaatlichen Museen in Bayern, and the Sparkassenstiftung. By providing access and enabling temporal hardware installations they allowed gathering data within a range of unique and representative public buildings, enhancing the quality of the developed system, and the experiments provided within this work.Thanks also go to Markus, Christin, Alexander, and Maximilian, not only for spending numerous hours of their spare time with proof reading and feedback, but also for all emotional support while conducting my research and writing this work. The same goes to my parents, for always supporting me, and my education.To Katharina, who has been a true friend, and will never be forgotten.

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Section: Chapter 2 Probabilistic Sensor Modelsmentioning

confidence: 99%

Smartphone-Based 3D Indoor Localization and Navigation

Ebner¹

2021

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“…1 For the purpose of this work, the transformation used from geodetic (latitude, longitude) to Cartesian (local) coordinates does not matter. One could use, for example, the Universal Transverse Mercator transformation with the WGS'84 ellipsoid [49] or the North-East-Down transformation of an earth-centered earth-fixed frame; see [66]. (a) Generation of differences (dx and dy) between the vehicle's actual position and the extrapolated position (generated on both sender and receiver using tracked positions previously transmitted).…”

Section: Approachmentioning

confidence: 99%

“…3.7) was hypothetically transmitted, but this time using the AlvinXY transformation from latitude and longitude to x and y [66]. The size of each delta message was computed for the same types of models as for the GLINT10 experiment; these results are plotted in Fig.…”

Section: Agave07mentioning

confidence: 99%

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

Schneider¹

2013

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Autonomous marine vehicles are increasingly used in clusters for an array of oceanographic tasks. The effectiveness of this collaboration is often limited by communications: throughput, latency, and ease of reconfiguration. This thesis argues that improved communication on intelligent marine robotic agents can be gained from acting on knowledge gained by improved awareness of the physical acoustic link and higher network layers by the AUV's decision making software.This thesis presents a modular acoustic networking framework, realized through a C++ library called goby-acomms, to provide collaborating underwater vehicles with an efficient short-range single-hop network. goby-acomms is comprised of four components that provide: 1) losslessly compressed encoding of short messages; 2) a set of message queues that dynamically prioritize messages based both on overall importance and time sensitivity; 3) Time Division Multiple Access (TDMA) Medium Access Control (MAC) with automatic discovery; and 4) an abstract acoustic modem driver.Building on this networking framework, two approaches that use the vehicle's "intelligence" to improve communications are presented. The first is a "non-disruptive" approach which is a novel technique for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. This system was analyzed on experimental data and implemented on a fielded vehicle. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved.The second approach is "disruptive," as it changes the vehicle's course to effect an improvement in the communications channel. A hybrid data-and model-based autonomous environmental adaptation framework is presented which allows autonomous underwater vehicles (AUVs) with acoustic sensors to follow a path which optimizes their ability to maintain connectivity with an acoustic contact for optimal sensing or communication.

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“…This delta transmission is sent continuously following a full 1 For the purpose of this work, the transformation used from geodetic (latitude, longitude) to Cartesian (local) coordinates does not matter. One could use, for example, the Universal Transverse Mercator transformation with the WGS'84 ellipsoid [13] or the NorthEast-Down transformation of an earth-centered earth-fixed frame; see [14]. transmission or prior delta transmissions until the vehicle was removed from operation for greater than τ seconds, after which a full transmission is sent to reinitialize the receiver's state.…”

Section: Approach a Application / Presentation Network Layersmentioning

confidence: 99%

“…8) was hypothetically transmitted, but this time using the AlvinXY transformation from latitude and longitude to x and y [14]. The size of each delta message was computed for the same types of models as for the GLINT10 experiment; these results are plotted in Fig.…”

Section: B Agave07mentioning

confidence: 99%

A State Observation Technique for Highly Compressed Source Coding of Autonomous Underwater Vehicle Position

Schneider

Schmidt

2013

IEEE J. Oceanic Eng.

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Abstract-A novel technique is presented for using state observers in conjunction with an entropy source encoder to enable highly compressed telemetry of autonomous underwater vehicle (AUV) position vectors. In this work, both the sending vehicle and receiving vehicle or human operator are equipped with a shared real-time simulation of the sender's state based on the prior transmitted positions. Thus, only the innovation between the sender's actual state and the shared state need be sent over the link, such as a very low throughput acoustic modem. The distribution of this innovation can be modeled a priori or assembled adaptively. This distribution is then paired with an arithmetic entropy encoder, producing a very low cost representation of the vehicle's position vector.This system was analyzed on experimental data from the GLINT10 and AGAVE07 expeditions involving two different classes of AUVs performing a diverse number of maneuvers, and implemented on a fielded vehicle in the MBAT12 experiment. Using an adaptive probability distribution in combination with either of two state observer models, greater than 90% compression, relative to a 32-bit integer baseline, was achieved.

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Rectilinear coordinate frames for Deep sea navigation

Cited by 6 publications

References 26 publications

Smartphone-Based 3D Indoor Localization and Navigation

Smartphone-Based 3D Indoor Localization and Navigation

Advances in integrating autonomy with acoustic communications for intelligent networks of marine robots

A State Observation Technique for Highly Compressed Source Coding of Autonomous Underwater Vehicle Position

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