Side scan sonar grid map for Unmanned Underwater Vehicle navigation

Chen, Edward; Huang, Sheng Wen; Wang, Wei-Han; Guo, Jenhwa

doi:10.23919/oceans.2011.6107035

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…However, the geographic information can easily be affected by environmental disturbances and the pose variance of positioning devices. These can degrade the quality of the final relief image [1][2][3]. The biases of the geographic information can be corrected by comparing the relative relationships of the images' positions using SSS image matching.…”

Section: Introductionmentioning

confidence: 99%

Combined imaging matching method of side scan sonar images with prior position knowledge

Tao

Liu

2018

IET Image Processing

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Section: Introductionmentioning

confidence: 99%

Combined imaging matching method of side scan sonar images with prior position knowledge

Tao

Liu

2018

IET Image Processing

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“…A common approach to produce geometrically meaningful maps is the occupancy grid [ 13 ]. Although occupancy grids are aimed at distinguishing free and occupied regions, similar approaches have been applied with relative success in the context of SSS mapping [ 14 , 15 ]. Unfortunately, the occupancy grid techniques still present some problems when used to construct dense SSS maps.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Underwater Mapping Using Side-Scan Sonar

Burguera

Oliver

2016

PLoS ONE

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The goal of this study is to generate high-resolution sea floor maps using a Side-Scan Sonar(SSS). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw sensor data is corrected by means of a physics-based SSS model. Second, the data is projected to the sea-floor. The errors involved in this projection are thoroughfully analysed. Third, a probabilistic SSS model is defined and used to estimate the probability of each sea-floor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution. Finally, a geometric map building method is presented and combined with the probabilistic approach. The resulting map is composed of two layers. The echo intensity layer holds the most likely echo intensities at each point in the sea-floor. The probabilistic layer contains information about how confident can the user or the higher control layers be about the echo intensity layer data. Experimental results have been conducted in a large subsea region.

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“…Because of the lack of natural light and low visibility, the traditional optical images had certain limitations for underwater exploration. As sonar imaging system can image in poorly visible water of turbidity, it is widely used in underwater exploration, such as marine mapping [1], [2], underwater object detection and tracking [3], [4], marine mineral resources exploration [5] and so on.…”

Section: Introductionmentioning

confidence: 99%

Denoising and tracking of sonar video imagery for underwater security monitoring systems

Luo

Liu

Huang

et al. 2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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As the acoustic images are low-quality, it is difficult to use these images for scientific research and practical applications directly. Although the PSNR of sonar images were improved through existing methods, denoised images were lack of clarity so that the outline of objects and details had not been better preserved. Subsequently, this impacted accuracy of target detection. In this paper, we propose a collaborative tracking algorithm based on Mean-Shift and reference-template-based matching (RTM). We construct a set of underwater security monitoring sonar system and carry out our experiment in Huangpu River. The algorithm represented in this paper can effectively improve the signal noise ratio of the sonar image, an increase strength of about 3 ~ 4db. Target tracking satisfies the real-time requirements, which shows our method is effective.

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Side scan sonar grid map for Unmanned Underwater Vehicle navigation

Cited by 8 publications

References 9 publications

Combined imaging matching method of side scan sonar images with prior position knowledge

Combined imaging matching method of side scan sonar images with prior position knowledge

High-Resolution Underwater Mapping Using Side-Scan Sonar

Denoising and tracking of sonar video imagery for underwater security monitoring systems

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