Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

Tan, Yew Teck; Hover, Franz S.

doi:10.1007/s10514-015-9508-2

Cited by 28 publications

(22 citation statements)

References 38 publications

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“…Numerous issues remain unresolved, a part of which are resulted from poorly understood EC-implications [52][53][54][55][56][57][58]. Brain-inspired modelling approach to such implications in the present study majorly depends on amounts of attention data and length of attention time, ignoring the differences in real scenarios.…”

Section: Simulation and Discussionmentioning

confidence: 96%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to +1 , = 1, 2, . . .) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

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Section: Simulation and Discussionmentioning

confidence: 96%

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

et al. 2018

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“…The robot got close to the harbor wall and thus moved over an unstructured and nonsmooth ground at around 90 s. estimation errors are unbounded in both cases. Low-cost and small-package navigation solutions with bounded error estimates are the subject of recent work [48], [49]. A combination of the DPSS with such solutions could lead to an accurate selfsustainable navigation suite completely independent from large and costly sensors.…”

Section: Lawn Mower and Loop Trajectoriesmentioning

confidence: 99%

Differential Pressure Sensor Speedometer for Autonomous Underwater Vehicle Velocity Estimation

Meurer

Fuentes‐Pérez

Palomeras

et al. 2020

IEEE J. Oceanic Eng.

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Velocity estimation is central for the reliable navigation of autonomous underwater vehicles (AUVs). Doppler velocity logs (DVLs), currently the leading technology for underwater velocity estimation, can be too big, expensive, and energy consuming to be used on low-cost and small AUVs or for long missions. In our previous work, a system based on differential pressure sensors was developed for estimating surge velocity. In this paper, we combine this system with an inertial measurement unit to compensate for orientation errors and create a differential pressure sensor speedometer (DPSS). We propose and demonstrate the DPSS prototype as an important step toward a small, inexpensive, and energy-efficient alternative or complement to a DVL in certain applications. This paper presents the first underwater field tests of a sensor using differential pressure for velocity estimation. Tests were conducted with a SPARUS II AUV (IQUA Robotics, Girona, Spain). To demonstrate the efficacy of our proposed solution, we compare the surge velocity estimation of the DPSS and the vehicle's DVL in bottom and water locks. Trials were conducted by varying the trajectory and velocity of the vehicle in three different environments. We show that the DPSS displayed a superior performance with respect to the DVL water lock for velocities above 0.6 m/s. The differences in the velocity estimations of the DVL in bottom lock for high velocities were as small as 0.013 m/s. These results encourage further development of the presented technology. Index Terms-Autonomous underwater vehicle (AUV), differential pressure sensors, Doppler velocity log (DVL), dead reckoning, flow sensing. I. INTRODUCTION F OR THE successful operation and recovery of autonomous underwater vehicles (AUVs), robust navigation and localization are crucial [1], [2]. However, the availability of sophisticated sensor systems for navigation and localization is restricted by the size, energy requirements, budget, and operation conditions of the particular vehicle. Especially with the increasing Manuscript

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“…Global positioning system (GPS) and acoustic baseline positioning systems, such as long baseline, short baseline or ultra‐short baseline systems, are traditionally used to provide additional positioning correction [2]. Unfortunately, these systems have their inherent disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Robust particle filter based on Huber function for underwater terrain‐aided navigation

Peng

Zhou

Folkesson

et al. 2019

IET Radar, Sonar & Navigation

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Terrain-aided navigation (TAN) is a promising technique to determine the location of underwater vehicle by matching terrain measurement against a known map. The particle filter (PF) is a natural choice for TAN because of its ability to handle non-linear, multimodal problems. However, the terrain measurements are vulnerable to outliers, which will cause the PF to degrade or even diverge. Modification of the Gaussian likelihood function by using robust cost functions is a way to reduce the effect of outliers on an estimate. The authors propose to use the Huber function to modify the measurement model used to set importance weights in a PF. They verify their method in simulations of multi-beam sonar in a real underwater digital map. The results demonstrate that the proposed method is more robust to outliers than the standard PF (SPF).

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Cooperative bathymetry-based localization using low-cost autonomous underwater vehicles

Cited by 28 publications

References 38 publications

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Differential Pressure Sensor Speedometer for Autonomous Underwater Vehicle Velocity Estimation

Robust particle filter based on Huber function for underwater terrain‐aided navigation

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