AUV for diver assistance and safety &amp;#x2014; Design and implementation

Stilinović, Nikola; Nađ, Ðula; Mišković, Nikola

doi:10.1109/oceans-genova.2015.7271670

Cited by 22 publications

(18 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For data collection the BUDDY-AUV was specifically designed by the University of Zagreb during the CADDY project [7]. The vehicle is fully actuated and is equipped with navigation sensors: Doppler Velocity Log (DVL), Ultra Short Baseline (USBL), GPS; and perception sensors: multibeam sonar and Bumblebee XB3 stereo camera.…”

Section: A Buddy Auvmentioning

confidence: 99%

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

Chavez

Ranieri

Chiarella

et al. 2019

JMSE

View full text Add to dashboard Cite

In this article we present a novel underwater dataset collected from several field trials within the EU FP7 project "Cognitive autonomous diving buddy (CADDY)", where an Autonomous Underwater Vehicle (AUV) was used to interact with divers and monitor their activities. To our knowledge, this is one of the first efforts to collect a large dataset in underwater environments targeting object classification, segmentation and human pose estimation tasks. The first part of the dataset contains stereo camera recordings (≈ 10K) of divers performing hand gestures to communicate and interact with an AUV in different environmental conditions. These gestures samples serve to test the robustness of object detection and classification algorithms against underwater image distortions i.e., color attenuation and light backscatter. The second part includes stereo footage (≈ 12.7K) of divers free-swimming in front of the AUV, along with synchronized IMUs measurements located throughout the diver's suit (DiverNet) which serve as ground-truth for human pose and tracking methods. In both cases, these rectified images allow investigation of 3D representation and reasoning pipelines from low-texture targets commonly present in underwater scenarios. In this paper we describe our recording platform, sensor calibration procedure plus the data format and the utilities provided to use the dataset.

show abstract

Section: A Buddy Auvmentioning

confidence: 99%

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

Chavez

Ranieri

Chiarella

et al. 2019

JMSE

View full text Add to dashboard Cite

show abstract

“…Fusion of USBL measurements with inertial sensors data and/or vehicle dynamics, used for accurate vehicle localization, is shown in [1,2]. In [3] the authors have used USBL to track white sharks with an autonomous underwater vehicle, and in [4] USBL tracking was used to track the diver with an autonomous surface vehicle.…”

Section: Introductionmentioning

confidence: 99%

Underwater Object Tracking Using Sonar and USBL Measurements

et al. 2016

View full text Add to dashboard Cite

In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.

show abstract

“…Estudos para arqueologia [71], [30], [72] Inspeção visual de estruturas [73], [43], [74], [75], [76] Veículos de intervenção [77], [78], [79], [80], [46] Veículos para competições [81] Plataformas de desenvolvimento [82], [83], [84], [85], [86], [35] Assistência à mergulhadores [87] sensores de parâmetros da água. [94], [49], [95], [50], [46], [96], [69], [97], [31], [98], [54], [55], [32], [81], [89], [23], [33], [99], [51], [60], [93], [22], [100], [39], [101], [102], [70], <...>…”

Section: Aplicação Referênciasunclassified

“…Sidescan [26], [23], [107], [102], [60], [58], [108] Batimetria [93], [107], [109], [79], [22], [40], [24], [73], [51], [105], [26], [88], [85], [102], [110], [111] Multi-feixe [26], [87], [49], [112], [50], [100], [63], [23], [71], [58], [108] Sub-bottom Profilers [81], [90], [94], [58] aplicações acima de 1000m, sendo o limite mais comum 3000m de profundidade. Um dos trabalhos apresenta o desenvolvimento de um veículo para profundidade de 11000m, capaz de suportar as maiores profundidades encontradas nos oceanos.…”

Section: Tipo Referênciaunclassified

“…Pequenos [82], [76], [115], [35], [75], [86], [70], [114], [47], [117], [100], [21], [53], [74], [87], [81], [71], [113], [22], [30], [110], [69], [68], [106], [25], [118], [60], [119], [121], [83], [85], [45], [59] Grandes [98], [102], [90], [89], [40], [93], [109], [63], [94], [97], [55], [42], [51], [54], [46] 3. Tabela 10 -Mecanismos de movimentação de veículos submarinos.…”

Section: Tamanho Referênciasunclassified

See 1 more Smart Citation

Desenvolvimento de veículos autônomos submarinos para aplicações oceanográficas.

Oliveira¹

View full text Add to dashboard Cite

show abstract

AUV for diver assistance and safety — Design and implementation

Cited by 22 publications

References 2 publications

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

CADDY Underwater Stereo-Vision Dataset for Human–Robot Interaction (HRI) in the Context of Diver Activities

Underwater Object Tracking Using Sonar and USBL Measurements

Desenvolvimento de veículos autônomos submarinos para aplicações oceanográficas.

Contact Info

Product

Resources

About