Underwater 3D reconstruction using BlueView imaging sonar

Guerneve, Thomas; Pétillot, Yvan

doi:10.1109/oceans-genova.2015.7271575

Cited by 25 publications

(22 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we unify our previous results (Guerneve & Petillot, ), considered state‐of‐the‐art on approximate 3D reconstruction using carving with a more fundamental formulation for the ill‐posed problem of 3D reconstruction from wide‐aperture sonar images. Then we perform a rigorous evaluation of both approaches, comparing their relative benefits.…”

Section: Review: Imaging Modelmentioning

confidence: 82%

“…Sonar carving using the min‐filter (Guerneve & Petillot, ) operates on a discretization of the 3D volume. Our deconvolution approach performs a discretization in 1D (along the U axis) for each chosen r and θ.…”

Section: Discussion Comparison and Limitationsmentioning

confidence: 99%

“…Most sonar scanning is limited to height fields (2.5D). Notable exceptions include a few prohibitively expensive direct 3D sonar sensors (Davis & Lugsdin, ) and recent methods in sonar carving (Guerneve & Petillot, ). However, their errors and coverage have not previously been analysed quantitatively.…”

Section: Discussion Comparison and Limitationsmentioning

confidence: 99%

“…As presented in our previous work (Guerneve & Petillot, ), the problem of 3D reconstruction from multiple observations can be solved using a nonlinear approximation akin to a carving method.…”

Section: D Reconstruction From Sonar Imagesmentioning

confidence: 99%

“…This can be achieved using pencil‐beam systems ( profilers ) exhibiting a very narrow aperture and a low spatial footprint or imagers which use a wider beam for general scene acquisition and understanding. Recent research work based on carving methods (Aykin & Negahdaripour, ; Guerneve & Petillot, ) demonstrated the feasibility of using acoustic imagers to obtain a 3D map of the environment, recovering enough information to enable manual object recognition. These systems, such as BlueView sonar or Didson acoustic cameras, provide high‐resolution imaging at short ranges, are widely available on existing underwater platforms, and have traditionally been employed to perform other tasks such as collision avoidance (Petillot et al., ), sea floor mapping (Hurtos, Nagappa, Palomeras, & Salvi, ), or target tracking (Folkesson, Leonard, Leederkerken, & Williams, ).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR

Guerneve

Subr

Pétillot

2018

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

The estimation of the geometric structure of objects located underwater underpins a plethora of applications such as mapping shipwrecks for archaeology, monitoring the health of coral reefs, detecting faults in offshore oil rigs and pipelines, detection and identification of potential threats on the seabed, etc. Acoustic imaging is the most popular choice for underwater sensing. Underwater exploratory vehicles typically employ wide-aperture Sound Navigation and Ranging (SONAR) imaging sensors. Although their wide aperture enables scouring large volumes of water ahead of them for obstacles, the resulting images produced are blurry due to integration over the aperture. Performing three-dimensional (3D) reconstruction from this blurry data is notoriously difficult. This challenging inverse problem is further exacerbated by the presence of speckle noise and reverberations. The state-of-the-art methods in 3D reconstruction from sonar either require bulky and expensive matrix-arrays of sonar sensors or additional narrow-aperture sensors. Due to its low footprint, the latter induces gaps between reconstructed scans. Avoiding such gaps requires slow and cumbersome scanning by the vehicles that carry the scanners. In this paper, we present two reconstruction methods enabling on-site 3D reconstruction from imaging sonars of any aperture. The first of these presents an elegant linear formulation of the problem, as a blind deconvolution with a spatially varying kernel. The second method is a simple algorithmic approach for approximate reconstruction, using a nonlinear formulation. We demonstrate that our simple approximation algorithms perform 3D reconstruction directly from the data recorded by wideaperture systems, thus eliminating the need for multiple sensors to be mounted on underwater vehicles for this purpose. Additionally, we observe that the wide aperture may be exploited to improve the coverage of the reconstructed samples (on the scanned object's surface). We demonstrate the efficacy of our algorithms on simulated as well as real data acquired using two sensors, and we compare our work to the state of the art in sonar reconstruction. Finally, we show the employability of our reconstruction methods on field data gathered by an autonomous underwater vehicle. K E Y W O R D Sdeconvolution, perception, SONAR processing, 3D reconstruction, underwater robotics

show abstract

Section: Review: Imaging Modelmentioning

confidence: 82%

Section: Discussion Comparison and Limitationsmentioning

confidence: 99%

Section: Discussion Comparison and Limitationsmentioning

confidence: 99%

Section: D Reconstruction From Sonar Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR

Guerneve

Subr

Pétillot

2018

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

Seabed Terrain 3D Reconstruction Using 2D Forward-Looking Sonar: A Sea-Trial Report From The Pipeline Burying Project

Song

et al. 2019

IFAC-PapersOnLine

View full text Add to dashboard Cite

Comparative analysis of range sensors for the robust autonomous navigation – a review

Singh

Nagla

2019

View full text Add to dashboard Cite

Purpose The purpose of this research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment. Design/methodology/approach Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot. Findings This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot. Originality/value This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.

show abstract

Underwater 3D reconstruction using BlueView imaging sonar

Cited by 25 publications

References 5 publications

Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR

Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR

Seabed Terrain 3D Reconstruction Using 2D Forward-Looking Sonar: A Sea-Trial Report From The Pipeline Burying Project

Comparative analysis of range sensors for the robust autonomous navigation – a review

Contact Info

Product

Resources

About