An analysis of real-time terrain aided navigation results from a HUGIN AUV

Ånonsen, Kjetil Bergh; Hagen, Ove Kent

doi:10.1109/oceans.2010.5664076

Cited by 32 publications

(12 citation statements)

References 12 publications

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“…A team at the Norwegian Defence Research Establishment carried out sea trials using TAN on an AUV with a multibeam and DVL. After 7 h of operation the team found that the navigation system was only 4 m out from a USBL system that was attached to a AUV and a research vessel that followed its movements [171]. These solutions can also be used in ROVs.…”

Section: Navigation and Positioning Sensorsmentioning

confidence: 99%

Inspection-Class Remotely Operated Vehicles—A Review

Capocci

Dooly

Omerdić

et al. 2017

JMSE

182

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This paper presents a review of inspection-class Remotely Operated Vehicles (ROVs). The review divides the classification of inspection-class ROVs; categorising the vehicles in order of size and capability. A state of the art technology review is undertaken, discussing various common subsystems of the ROV. Standard and novel ROV shapes and designs are reviewed, with emphasis on buoyancy, frame materials and hydrodynamics. Several power considerations and designs are discussed, accounting for battery fed and mains fed systems. ROV telemetry is split into a discussion on the various transmission hardware systems and the communication protocols that are most widely used in industry and research today. A range of thruster technologies is then introduced with consideration taken of the various thruster architectures available. Finally, the navigation and positioning sensors employed for ROV navigation and control are reviewed. The author has also created a number of comparison tables throughout the review; tables include comparison of wired data transmission technology, comparison of common ROV communication protocols and comparisons of various inertial navigation systems. By the end of the review the reader will have clearer understanding on the fundamentals of inspection-class ROV technologies and can use this as an introduction to further paper investigation.

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Section: Navigation and Positioning Sensorsmentioning

confidence: 99%

Inspection-Class Remotely Operated Vehicles—A Review

Capocci

Dooly

Omerdić

et al. 2017

JMSE

182

View full text Add to dashboard Cite

show abstract

“…It is more common that an a priori map is not available-either the mission is to explore a new area, or to measure and characterize changes in the terrain. In this case, the vehicle might generate incremental maps from sensor data on the fly (e.g., [10,36]) or use simultaneous localization and mapping (SLAM) techniques (e.g., [37][38][39]). …”

Section: Terrain-relative Navigationmentioning

confidence: 99%

“…The terrain may be characterized by sonar range measurements to the seafloor [10,32,33,36,[38][39][40][41] or photographs of the seafloor [42][43][44][45]. For some missions, AUVs have navigated relative to sonar maps of moving targets like icebergs [46,47].…”

Section: Terrain-relative Navigationmentioning

confidence: 99%

Contributions to automated realtime underwater navigation

Stanway¹

2012

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This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas.The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors.The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. 3Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjust...

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“…The results from the Oslofjord 2010 [15] runs indicate that some tuning must be done to adapt the tests to other sensors. This tuning can be in the form of different rejection limits or some kind of scaling of the statistics depending on which sensor is used.…”

Section: Integrity Systemmentioning

confidence: 99%

The HUGIN real-time terrain navigation system

Hagen

Ånonsen

Mandt

2010

Oceans 2010 MTS/Ieee Seattle

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Submerged long endurance autonomous missions are a real challenge for the navigation systems of autonomous underwater vehicles (AUV). Terrain navigation is a promising technique for obtaining submerged position updates for the navigation system. This paper describes a real-time terrain navigation system developed for the HUGIN AUV, and reports of sea trials, where HUGIN 1000 HUS was navigating accurately in real-time with terrain navigation as the only source for position updates during long transit legs.

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An analysis of real-time terrain aided navigation results from a HUGIN AUV

Cited by 32 publications

References 12 publications

Inspection-Class Remotely Operated Vehicles—A Review

Inspection-Class Remotely Operated Vehicles—A Review

Contributions to automated realtime underwater navigation

The HUGIN real-time terrain navigation system

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