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

Abstract

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

Development of a Control System for Multilink Manipulators on Unmanned Underwater Vehicles Dynamically Positioned over Seafloor Objects

“…To identify a certain known object in the point cloud obtained from MVS, the work [18] suggests using a 3D model of this object, preprocessed and transformed into a point cloud set in the CS Оxyz. This cloud, consisting of m points 𝐾 𝑞 ∈ 𝑅 3 (𝑞 = 1, 𝑚), first, is virtually approximated to the cloud of points belonging to the actual object through the linear transfer in the direction of the vector 𝑝 ⃗ 1 ∈ 𝑅 3 , whose magnitude and direction are determined by the systems of detection of underwater objects that stand out from the surrounding [9,10]. Then the displaced cloud, defined by the points 𝑃 𝑞 = (𝐾 𝑞 + 𝑝 ⃗ 1 ), is aligned with the point cloud of the object using the iterative closest point (ICP) algorithm [29] implemented in the open-access Point Cloud Library (PCL) [30].…”

“…Their efficiency has been confirmed experimentally. Prior to the operations, the object is identified on the basis of data received from the onboard sonars [9,10], and then the UUV is sent to the object. Simultaneously, a site for stabilized hovering of the UUV is selected in such a way that the working zone of the manipulator allows correct execution of the required operations and avoidance of collisions of the vehicle with the bottom or object [8].…”

Underwater navigation with 2D forward looking SONAR: An adaptive unscented Kalman filter‐based strategy for AUVs

“…Forward‐looking SONARs (FLSs) are commonly employed in 2D mosaicing (Ferreira et al, 2015; Hurtós et al, 2015), automatic target recognition (ATR; Valdenegro‐Toro, 2019), 3D mapping and surface reconstruction (Aykin & Negahdaripour, 2013; Guerneve et al, 2018), and navigation‐aiding (Franchi et al, 2020; Westman & Kaess, 2019). In the context of navigation‐aiding, these devices have been often relegated, for the majority of times, to bound the navigation drift, working along with a standard navigation sensor set constituted, for example, of a Doppler velocity log (DVL) and an attitude heading reference system (AHRS).…”

Imaging sonar simulator for assessment of image registration techniques

“…1). Using polar coordinates, the intensity of a pixel on a sonar image can be represented by [3] where a single pixel is formed by the contribution of all the intensities from points in three-dimensional (3D) space (r, θ, φ) over the aperture [φ 1 , φ 2 ]. β(φ) is a function related to the beam pattern, V s (r, θ, φ) is a measure related to objects reflectivity and D s (r, θ, φ) = v• n rθφ v n rθφ , is the cosine of the angle between the direction of the beam v and the surface normal n at point (r, θ, φ).…”