Pose-Graph SLAM for Underwater Navigation

Sensing and Control for Autonomous Vehicles - Lecture Notes in Control and Information Sciences ◽

10.1007/978-3-319-55372-6_7 ◽

2017 ◽

pp. 143-160 ◽

Cited By ~ 3

Author(s):

Stephen M. Chaves ◽

Enric Galceran ◽

Paul Ozog ◽

Jeffrey M. Walls ◽

Ryan M. Eustice

Keyword(s):

Underwater Navigation ◽

Graph Slam

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AUV Bathymetric Simultaneous Localisation and Mapping Using Graph Method

Journal of Navigation ◽

10.1017/s0373463319000286 ◽

2019 ◽

Vol 72 (06) ◽

pp. 1602-1622

Author(s):

Teng Ma ◽

Ye Li ◽

Yusen Gong ◽

Rupeng Wang ◽

Mingwei Sheng ◽

...

Keyword(s):

Autonomous Underwater Vehicles ◽

Data Association ◽

Underwater Vehicles ◽

Geological Surveys ◽

Underwater Navigation ◽

Graph Slam ◽

Mapping Techniques ◽

Terrain Matching ◽

Global And Local

Although topographic mapping missions and geological surveys carried out by Autonomous Underwater Vehicles (AUVs) are becoming increasingly prevalent, the lack of precise navigation in these scenarios still limits their application. This paper deals with the problems of long-term underwater navigation for AUVs and provides new mapping techniques by developing a Bathymetric Simultaneous Localisation And Mapping (BSLAM) method based on graph SLAM technology. To considerably reduce the calculation cost, the trajectory of the AUV is divided into various submaps based on Differences of Normals (DoN). Loop closures between submaps are obtained by terrain matching; meanwhile, maximum likelihood terrain estimation is also introduced to build weak data association within the submap. Assisted by one weight voting method for loop closures, the global and local trajectory corrections work together to provide an accurate navigation solution for AUVs with weak data association and inaccurate loop closures. The viability, accuracy and real-time performance of the proposed algorithm are verified with data collected onboard, including an 8 km planned track recorded at a speed of 4 knots in Qingdao, China.

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Guidance and Control, for Small AUVs Using DGPS and Doppler Aided Inertial Underwater Navigation

10.21236/ada436008 ◽

2005 ◽

Author(s):

Anthony J. Healey

Keyword(s):

Guidance And Control ◽

Underwater Navigation ◽

And Control

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Implementation of translational motion dynamics for INS data fusion in DVL outage in underwater navigation

IEEE Sensors Journal ◽

10.1109/jsen.2020.3042902 ◽

2020 ◽

pp. 1-1

Author(s):

Ali Karmozdi ◽

Mojtaba Hashemi ◽

Hassan Salarieh ◽

Aria Alasty

Keyword(s):

Data Fusion ◽

Translational Motion ◽

Underwater Navigation ◽

Motion Dynamics

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Underwater navigation system with velocity measurement by a horizon Kalman filter

Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600) ◽

10.1109/oceans.2004.1406364 ◽

2005 ◽

Cited By ~ 1

Author(s):

GyungNam Jo ◽

H.S. Choi

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Velocity Measurement ◽

Underwater Navigation

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Sensor Modeling for Underwater Localization Using a Particle Filter

Sensors ◽

10.3390/s21041549 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1549

Author(s):

Humberto Martínez-Barberá ◽

Pablo Bernal-Polo ◽

David Herrero-Pérez

Keyword(s):

Sequential Monte Carlo ◽

Sensory Information ◽

Dead Reckoning ◽

Underwater Vehicle ◽

Position Estimation ◽

Sources Of Uncertainty ◽

Sensor Signals ◽

Underwater Navigation ◽

Underwater Localization ◽

Sensor Modeling

This paper presents a framework for processing, modeling, and fusing underwater sensor signals to provide a reliable perception for underwater localization in structured environments. Submerged sensory information is often affected by diverse sources of uncertainty that can deteriorate the positioning and tracking. By adopting uncertain modeling and multi-sensor fusion techniques, the framework can maintain a coherent representation of the environment, filtering outliers, inconsistencies in sequential observations, and useless information for positioning purposes. We evaluate the framework using cameras and range sensors for modeling uncertain features that represent the environment around the vehicle. We locate the underwater vehicle using a Sequential Monte Carlo (SMC) method initialized from the GPS location obtained on the surface. The experimental results show that the framework provides a reliable environment representation during the underwater navigation to the localization system in real-world scenarios. Besides, they evaluate the improvement of localization compared to the position estimation using reliable dead-reckoning systems.

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