Dense, Sonar-based Reconstruction of Underwater Scenes

Teixeira, Pedro Nuno Vaz

doi:10.1575/1912/24771

Cited by 4 publications

(3 citation statements)

References 79 publications

(122 reference statements)

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“…While all of the above case studies are successful, [5], [7] and [10] rely on a ring laser gyroscope for highly accurate, low-drift heading information. The inertial package in [8] also shows highly accurate results as it is a simulation derivative of the INS in [5], [7], [10]. Lastly, [6] and [9] use SLAM to address missing, or noisy inertial information, showing improvement, but with drift still present.…”

Section: A Underwater Slam With Sonarmentioning

confidence: 96%

“…Ship hull inspection using a forward-looking imaging sonar is demonstrated in [5], multibeam profiling sonar SLAM traversing an underwater canyon is achieved in [6], and planar SLAM using imaging sonar observations of the seafloor is described in [7]. Additionally, [8] uses a factor graph to perform dense reconstruction of complex 3D structures using multibeam profiling sonar. Although underwater landmarkbased SLAM with sonar has also been implemented successfully, [9], [10], the challenge of landmark SLAM with acoustic sensors is the identification of point features, or the identification of objects as landmarks, and their subsequent, repeated association as they are re-observed.…”

Section: A Underwater Slam With Sonarmentioning

confidence: 99%

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Overhead Image Factors for Underwater Sonar-based SLAM

McConnell¹,

Chen²,

Englot³

2022

Preprint

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Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globallyreferenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. Video attachment: https://youtu.be/_uWljtp58ks

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Section: A Underwater Slam With Sonarmentioning

confidence: 96%

Section: A Underwater Slam With Sonarmentioning

confidence: 99%

Overhead Image Factors for Underwater Sonar-based SLAM

McConnell¹,

Chen²,

Englot³

2022

Preprint

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“…An imaging sonar based SLAM and 3D photomosaicing algorithm has been proposed by Westman et al (2018) and Ozog et al (2015), respectively. Teixeira et al (2019) presented dense reconstruction of underwater scenes using a multibeam sonar. McConnell et al (2020) fused two multibeam sonars, one placed horizontally and one vertically, to address the uncertainty over the elevation angle.…”

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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