Terrain Correlation Correction Method for AUV Seabed Terrain Mapping

Li, Ye; Wang, Rupeng; Chen, Pengyun; Zhang, Qiang

doi:10.1017/s037346331700011x

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…The main global navigational satellite system (GNSS) positioning method for submersible vehicles is limited to situations where the submersible vehicle can raise an antenna above the surface of the water. However, some AUVs need the more independent method of comparative (terrain) navigation via digital terrain models (DTMs) [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

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Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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“…The intercommunication components design has been done considering the long-range transmission, data volume, data heterogeneity transmission speed and the transmission medium between devices [42]. It is possible to define various communication technologies depending on the functionality or attribution of them.…”

Section: System Architecturementioning

confidence: 99%

Use of UIoT for Offshore Surveys Through Autonomous Vehicles

Sánchez

Márquez

Govindara

et al. 2021

Polish Maritime Research

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The ENDURUNS project is a European Research project of the Horizon 2020 framework, which has as its main objective to achieve the optimum and intelligent use of green hydrogen energy for long-term ocean surveys. The ENDURUNS system comprises an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV) with gliding capability. The power pack of the USV integrates Li-ion batteries with photovoltaic panels, whilst the AUV employs Li-ion batteries and a hydrogen fuel cell. It is essential to develop a continuous monitoring ca-pability for the different systems of the vehicles. Data transmission between the devices onboard presents challenges due to the volume and structure of the different datasets. A telecommunications network has been designed to manage the operational components considered in the project. The autonomous vehicles perform measurements, providing their position and other data wirelessly. The system will generate a great volume of various signals during the survey. The Remote Control Centre needs to be interfaced with the vehicles in order to receive, manage and store the acquired data. An Underwater Internet of Things (IoT) platform is designed to establish efficient and smart data management. This study presents an exhaustive survey to analyse the telecommunication systems employed in the autonomous vehicles, including the back-end, user interface and mobile units. This paper presents the novel design of the hardware and software structure of the ENDURUNS project with regard to the literature, where its components and their in-terconnection layers are detailed, which is a novel scientific and technological approach for autonomous seabed surveying in deep oceans or in coastal areas.

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“…The corrections of the estimated position of the vehicle (denoted as X (t) ) corresponding to U (n) are given by the terrain correlation correcting method [26]:…”

Section: A Map Updatementioning

confidence: 99%

Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

Zhang

Zheng

et al. 2021

IEEE Access

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To obtain independent navigation results for autonomous underwater vehicles (AUVs) and construct high-resolution consistent seabed maps, a particle filter-based bathymetric simultaneous localization and mapping (BSLAM) method with the mean trajectory map representation is proposed. To reduce the computational consumption, particles only keep the current estimated position of the vehicle, while all historical states of the vehicle are stored in the mean trajectory map. Using this set-up, only the weights of the particles which closed to the mean trajectory map are calculated with newly collected bathymetric data. A hierarchical clustering procedure is also discussed to identify invalid loop closures. The performance of the proposed method is validated using both the simulated data and the field data collected from sea trails. The results demonstrate that the proposed method is 50% more accurate and 50% faster than a state-ofthe-art particle filter-based BSLAM method, and it has similar accuracy but 30% faster compared with a graph-based BSLAM method.INDEX TERMS Autonomous underwater vehicle, bathymetric simultaneous localization and mapping, particle filter, hierarchical clustering.

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Terrain Correlation Correction Method for AUV Seabed Terrain Mapping

Cited by 11 publications

References 19 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Use of UIoT for Offshore Surveys Through Autonomous Vehicles

Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

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