Doppler water-track aided inertial navigation for autonomous underwater vehicle

Hegrenæs, Øyvind; Berglund, E.

doi:10.1109/oceanse.2009.5278307

Cited by 60 publications

(35 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Hegrenaes and Hallingstad (2011) (Hegrenaes and Berglund (2009)). The assumption is a time-varying current in the measured water sampling volume.…”

Section: Vehicle Model Based Navigationmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

View full text Add to dashboard Cite

Survey-class Autonomous Underwater Vehicles (AUVs) typically rely on Doppler Velocity Logs (DVL) for precision localization near the seafloor. In cases where the seafloor depth is greater than the DVL bottom-lock range, localizing between the surface and the seafloor presents a localization problem since both GPS and DVL observations are unavailable in the midwater column. This work proposes a solution to this problem that exploits the fact that current profile layers of the water column are near constant over short time scales (in the scale of minutes). Using observations of these currents obtained with the Acoustic Doppler Current Profiler (ADCP) mode of the DVL during descent, along with data from other sensors, the method discussed herein constrains position error. The method is validated using field data from the Sirius AUV coupled with view-based Simultaneous Localization and Mapping (SLAM) and on descents up to 3km deep with the Sentry AUV.

show abstract

“…In Hegrenaes and Hallingstad (2011) (Hegrenaes and Berglund (2009)). The assumption is a time-varying current in the measured water sampling volume.…”

Section: Vehicle Model Based Navigationmentioning

confidence: 99%

Mid-water current aided localization for autonomous underwater vehicles

et al. 2016

View full text Add to dashboard Cite

show abstract

“…This approach can also be implemented in conjunction with existing current-aided navigation schemes aiming at more accurate vehicle velocity tracking (e.g. [33] or [37]) to further improve the navigation performance of an AUV. In long-distance, mid-depth AUV missions where neither frequent surfacing nor constant bottom-tracking is available, the proposed current-aided navigation concept can be applied to improve the inertial navigation performance.…”

Section: Evaluation With Field Test Datamentioning

confidence: 99%

“…By closing this knowledge gap, researchers have been utilizing the background flow dynamics in various aspects of underwater robotics, including path planning and vehicle guidance [20]- [22], multi-vehicle cooperative control [23]- [25], optimal sensing [26]- [29], sensor network mobility analysis [30], [31], and mobile sensor allocation [32]. Nonetheless, to the best of our knowledge, the background flow dynamics has not been well exploited for underwater localization and navigation problems with only a few exceptions [33]- [37].…”

Section: Introductionmentioning

confidence: 99%

“…Medagoda et al [34], [37] focused on the navigation performance during vehicle descent by utilizing the fact that ocean currents are approximately invariant within a short period of time. Similarly, Hegrenaes and Berglund [33] only considered their DVL water-tracking technique as an intermediate approach to bridge the gap between other more reliable global localization techniques involving GPS or USBL. By including the knowledge of ocean general circulation, we aim to provide long-term performance improvement over inertial navigation systems alone.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

Song

Mohseni

2018

IEEE J. Oceanic Eng.

View full text Add to dashboard Cite

A current-aided inertial navigation framework is proposed for small autonomous underwater vehicles in longduration operations (> 1 hour), where neither frequent surfacing nor consistent bottom-tracking are available. We instantiate this concept through mid-depth, underwater navigation. This strategy mitigates dead-reckoning uncertainty of a traditional inertial navigation system by comparing the estimate of local, ambient flow velocity with preloaded ocean current maps. The proposed navigation system is implemented through a marginalized particle filter where the vehicle's states are sequentially tracked along with sensor bias and local turbulence that is not resolved by general flow prediction. The performance of the proposed approach is first analyzed through Monte Carlo simulations in two artificial background flow fields, resembling real-world ocean circulation patterns, superposed with smaller-scale, turbulent components with Kolmogorov energy spectrum. The current-aided navigation scheme significantly improves the dead-reckoning performance of the vehicle even when unresolved, small-scale flow perturbations are present. For a 6-hour navigation with an automotive-grade inertial navigation system, the currentaided navigation scheme results in positioning estimates with under 3% uncertainty per distance traveled (UDT) in a turbulent, double-gyre flow field, and under 7.3% UDT in a turbulent, meandering jet flow field. Further evaluation with field test data and actual ocean simulation analysis demonstrates consistent performance for a 6-hour mission, positioning result with under 25% UDT for a 24-hour navigation when provided direct heading measurements, and terminal positioning estimate with 16% UDT at the cost of increased uncertainty at an early stage of the navigation.

show abstract

“…For the feature of self-contained and continuity of navigation information, the SINS becomes a key navigation equipment for the underwater vehicles' navigation [3,4,5,6]. However, owing to the drift of the IMU measurements, a single SINS cannot afford precise position and velocity for a long period of time without any aiding methods.…”

Section: Introductionmentioning

confidence: 99%

A fast in-situ SINS and Doppler sensor calibration algorithm for underwater vehicle navigation

Song

2014

IEICE Electron. Express

View full text Add to dashboard Cite

This paper reports the development and experimental evaluation of an in-situ calibration algorithm of the misalignment mounting angle matrix between the Strapdown inertial navigation system (SINS) and Doppler sensor which are practically used for accurate underwater navigation. Most previously reported methods required the SINS to be aligned first to output accurate attitude before the calibration. By separately treating the body frame and the navigation frame attitude update, the algorithm in this paper could be simultaneously carried out during the SINS attitude alignment stage. Simulation and experiment results show that the calibration could be done in 300 s and the position error during the navigation is less than 0.8% of the voyage distance.

show abstract

Doppler water-track aided inertial navigation for autonomous underwater vehicle

Cited by 60 publications

References 8 publications

Mid-water current aided localization for autonomous underwater vehicles

Mid-water current aided localization for autonomous underwater vehicles

Long-Term Inertial Navigation Aided by Dynamics of Flow Field Features

A fast in-situ SINS and Doppler sensor calibration algorithm for underwater vehicle navigation

Contact Info

Product

Resources

About