Sensor fusion of two sonar devices for underwater 3D mapping with an AUV

Joe, Hangil; Cho, Hyeonwoo; Sung, Minsung; Kim, Jinwhan; Yu, Son-Cheol

doi:10.1007/s10514-021-09986-5

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…In the pointcloud map M , the slope of the points can be derived [ 12 ]. Given that the derivatives between the points generated in the X-Z plane are as follows:

where

and

is constant, then the slope is obtained:

where s is a beamwidth of an acoustic wave.…”

Section: Methodsmentioning

confidence: 99%

“…For

, we can simplify it by extracting the range information of the FLS in the sonar image. We can apply the difference filter after Gaussian filtering on the ROI

[ 12 ] as follows:

where

…”

Section: Methodsmentioning

confidence: 99%

“…To prevent unnecessary rerouting, Cho et al proposed an incremental 3D pointcloud generation method with a forward-moving AUV [ 11 ]; however, vertical ambiguity due to the vertical beamwidth caused an improper slope in the front area of the object of interest, thus degrading the quality of the resulting pointcloud. To improve the quality of the generated pointcloud, Joe et al proposed another method for underwater 3D reconstruction, which uses two sonar devices with complementary information [ 12 ]; this method requires a segmentation process for the front slope of an object in the pointcloud, which increases the computational load. In addition, a Monte-Carlo-based approach for 3D reconstruction was introduced in [ 13 ], which presented a method using a likelihood map generated by profiling sonar (PS) data and utilized it for reconstructing the elevation information of FLS data.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic 3D Reconstruction Using Two Sonar Devices

Joe

Kim

2022

Sensors

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Three-dimensional reconstruction is a crucial technique for mapping and object-search tasks, but it is challenging in sonar imaging because of the nature of acoustics. In underwater sensing, many advanced studies have introduced approaches that have included feature-based methods and multiple imaging at different locations. However, most existing methods are prone to environmental conditions, and they are not adequate for continuous data acquisition on moving autonomous underwater vehicles (AUVs). This paper proposes a sensor fusion method for 3D reconstruction using acoustic sonar data with two sonar devices that provide complementary features. The forward-looking multibeam sonar (FLS) is an imaging sonar capable of short-range scanning with a high horizontal resolution, and the profiling sonar (PS) is capable of middle-range scanning with high reliability in vertical information. Using both sonars, which have different data acquisition planes and times, we propose a probabilistic sensor fusion method. First, we extract the region of interest from the background and develop a sonar measurement model. Thereafter, we utilize the likelihood field generated by the PS and estimate the elevation ambiguity using importance sampling. We also present the evaluation of our method in a ray-tracing-based sonar simulation environment and the generation of the pointclouds. The experimental results indicate that the proposed method can provide a better accuracy than that of the conventional method. Because of the improved accuracy of the generated pointclouds, this method can be expanded for pointcloud-based mapping and classification methods.

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“…In the pointcloud map M , the slope of the points can be derived [ 12 ]. Given that the derivatives between the points generated in the X-Z plane are as follows:

where

and

is constant, then the slope is obtained:

where s is a beamwidth of an acoustic wave.…”

Section: Methodsmentioning

confidence: 99%

“…For

, we can simplify it by extracting the range information of the FLS in the sonar image. We can apply the difference filter after Gaussian filtering on the ROI

[ 12 ] as follows:

where

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic 3D Reconstruction Using Two Sonar Devices

Joe

Kim

2022

Sensors

Self Cite

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show abstract

“…McConnell et al (2020) fused two multibeam sonars, one placed horizontally and one vertically, to address the uncertainty over the elevation angle. Similarly, Joe et al (2021) proposed a mapping sensor configuration of multibeam sonar and profiling sonar exploiting the larger field-of-view covered by the first and the narrow beam of the latter.…”

Section: Related Workmentioning

confidence: 99%

SVIn2: A multi-sensor fusion-based underwater SLAM system

Rahman

Rekleitis

2022

The International Journal of Robotics Research

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This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles.

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“…With the development of ocean exploration, more precisely the way the position and movement information of the submersible should be transmitted in the dim, topographic, and ecologically complex underwater environment poses a huge challenge to underwater detection technology. Simultaneously, the commonly used detection methods (i.e., the sonar technology [ 3 ]) also face challenges, such as the high frequency of sound waves, poor bio-friendliness, low degree of modularity and systemization, high cost, and the requirement of significant maintenance [ 4 , 5 ]. Therefore, there is an urgent need to develop the novel underwater sensors [ 6 ], in order to match the ever-increasing demand for undersea detection, rescue, and defense.…”

Section: Introductionmentioning

confidence: 99%

Underwater Monitoring Networks Based on Cable-Structured Triboelectric Nanogenerators

Zhang

Cheng

et al. 2022

Research

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The importance of ocean exploration and underwater monitoring is becoming vital, due to the abundant biological, mineral, energy, and other resources in the ocean. Here, a self-powered underwater cable-based triboelectric nanogenerator (TENG) is demonstrated for underwater monitoring of mechanical motion/triggering, as well as searching and rescuing in the sea. Using a novel double-layer winding method combined with ferroelectric polarization, a self-powered cable-structured sensor with a stable electrical output has been manufactured, which can accurately respond to a variety of external mechanical stimuli. A self-powered cable sensing network woven using smart cables can comprehensively transmit information, such as the plane position and dive depth of a submersible. More precisely, it can analyze its direction of movement, speed, and path, along with transmitting information such as the submersible’s size and momentum. The developed self-powered sensor based on the cable-based TENG not only has low cost and simple structure but also exhibits working accuracy and stability. Finally, the proposed work provides new ideas for future seabed exploration and ocean monitoring.

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Sensor fusion of two sonar devices for underwater 3D mapping with an AUV

Cited by 22 publications

References 14 publications

Probabilistic 3D Reconstruction Using Two Sonar Devices

Probabilistic 3D Reconstruction Using Two Sonar Devices

SVIn2: A multi-sensor fusion-based underwater SLAM system

Underwater Monitoring Networks Based on Cable-Structured Triboelectric Nanogenerators

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