AUV-Based Underwater 3-D Point Cloud Generation Using Acoustic Lens-Based Multibeam Sonar

Cho, Hyeonwoo; Kim, Byeongjin; Yu, Son-Cheol

doi:10.1109/joe.2017.2751139

Cited by 58 publications

(29 citation statements)

References 39 publications

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“…Although it is possible to estimate the exact position at various altitudes, there are disadvantages that the position can be estimated only in the limited area where the landmark is installed and it should be installed in advance. The following method assumes that the seabed is sufficiently flat, and then the altitude of the UUV which is measured by using the altimeter (installed to the UUV) is applied as the altitude of the target area (where FSS is looking) [7], [8], [10]. This method is very simple to apply and is quite accurate when the seabed is actually flat.…”

Section: B Difficulties and Contributionsmentioning

confidence: 99%

“…Of these regions, the shadow region is an area where the acoustic beam is blocked due to the object, and the elevation of these objects can be reconstructed based on the shadow length [8], [9]. These methods are applicable only when there are remarkable objects in the sonar image [10]. However, in the case of general surveys, 3D reconstruction must be performed continuously irrespective of the presence of objects in the sonar image.…”

Section: Introductionmentioning

confidence: 99%

“…Cho et al propose a continuous 3D reconstruction method using the geometry of FSSs and mobility of the UUV [10]. The proposed method is developed for UUV-based object detection on the seafloor.…”

Section: Introductionmentioning

confidence: 99%

“…This combination enables to estimate the FSE of the FSS. The 3D reconstruction method proposed by Cho et al, is based on the assumption that the FSE and the measured downward altitude from the DVL are the same [10]. However, if the measured altitude of the UUV differs from the actual FSE, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

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On-Site Calibration-Based Estimation Method of Forward Seabed Elevation Using Forward Scan Sonar

Pyo

Cho

2019

IEEE Sensors J.

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This paper addresses a novel calibration based estimation method to measure the forward seabed elevation (FSE) where an area illuminated by forward scan sonar (FSS) for unmanned underwater vehicles (UUVs).The conventional method measures the FSE using one or two down-facing single-beam sonars; this method can, however, cause errors. In order to accurately measure the FSE, a sensor capable of measuring the two-dimensional space of the seabed is needed, such as FSS. The method proposed in this work consists in estimating the FSE by analyzing the acoustic beam distribution characteristics of FSS. A novel FSS beam distribution model is proposed, which simplifies beam distribution characteristics. Then, on-site calibration is conducted to estimate the parameters of this model. Based on the estimated model, templates can be generated and compared with FSS data acquired at the experiment in order to estimate geometrical information at the FSE. The FSE of the best matched template is the estimated FSE of the FSS data acquired in the experiment. In order to verify the performance of the proposed method, experiments using an UUV were carried out in an indoor water tank. We also verified that the proposed method performed well even when there were objects on the seabed.Index Terms-On-site calibration, Modeling of forward scan sonar, Localization of UUV, Forward seabed elevation.

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Section: B Difficulties and Contributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On-Site Calibration-Based Estimation Method of Forward Seabed Elevation Using Forward Scan Sonar

Pyo

Cho

2019

IEEE Sensors J.

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“…Lorenson et al [15] formed the 3D model of an object based on voxels and recognized the object by two-dimensional (2D) coding from the constructed model. Cho et al [16] developed a method to detect objects by generating the 3D point cloud of underwater objects from successive images acquired using an AUV's mobility. The 3D data such as the shape, range, and direction of object enable a more reliable object detection.…”

Section: Introductionmentioning

confidence: 99%

Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

et al. 2019

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This study proposes the detection and removal of crosstalk noise using a convolutional neural network in images of forward scan sonar. Because crosstalk noise occurs near an underwater object and distorts the shape of the object, underwater object detection is limited. The proposed method can detect crosstalk noise using the neural network and remove crosstalk noise based on the detection result. Thus, the proposed method can be applied to other sonar-image-based algorithms and enhance the reliability of those algorithms. We applied the proposed method to a three-dimensional point cloud generation and generated a more accurate point cloud. We verified the performance of the proposed method by performing multiple indoor and field experiments.

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Sensor‐driven autonomous underwater inspections: A receding‐horizon RRT‐based view planning solution for AUVs

Zacchini

Franchi

Ridolfi

2022

Journal of Field Robotics

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Autonomous Underwater Vehicles (AUVs) are used by the scientific community for various applications, from collecting well-distributed high-quality data to mapping the seafloor or exploring unknown areas. Nonpredictable environmental conditions and sensor acquisitions make the design of AUV surveys challenging even for expert operators. Multiple attempts are required, and the collected data quality is not guaranteed: The AUV passively stores the sensors' acquisitions that are then analyzed offline after its recovery. In Forward-Looking SONAR (FLS) seabed inspections, the vehicle follows lawnmower paths designed by an expert operator that considers the sensor characteristics. The performance of FLSs is affected by several environmental conditions and possible protruding objects. This paper presents a probabilistic framework for FLS-based seabed inspections that endow the AUV with the ability to autonomously conducting the survey and ensure adequate coverage of the target area. A three-dimensional probabilistic occupancy mapping system for FLS reconstructions to update the covered area map was developed. The map is used by the Coverage Path Planning (CPP) algorithm to evaluate the visibility of the viewpoints that are generated as nodes of a random tree. The Next-Best Viewpoint (NBV) is selected as the first node in the branch expected to collect more data, and the path to reach the NBV is computed using the rapidly exploring random tree (RRT*) algorithm. The sensor-driven coverage approach is used in a receding-horizon manner. The proposed Receding-Horizon Coverage Approach was validated with simulations and real prerecorded data. Finally, the framework was used online during an experimental campaign where several FLS seabed inspections were performed.

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AUV-Based Underwater 3-D Point Cloud Generation Using Acoustic Lens-Based Multibeam Sonar

Cited by 58 publications

References 39 publications

On-Site Calibration-Based Estimation Method of Forward Seabed Elevation Using Forward Scan Sonar

On-Site Calibration-Based Estimation Method of Forward Seabed Elevation Using Forward Scan Sonar

Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

Sensor‐driven autonomous underwater inspections: A receding‐horizon RRT‐based view planning solution for AUVs

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