A Modular Sensor Suite for Underwater Reconstruction

Rahman, Sharmin; Karapetyan, Nare; Li, Alberto Quattrini; Rekleitis, Ioannis

doi:10.1109/oceans.2018.8604819

Cited by 13 publications

(10 citation statements)

References 14 publications

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“…Moreover, it is compared to other state-of-the-art methods, i.e., VINS-Mono [10], the basic OKVIS [9], and the MSCKF [11] implementation from the GRASP lab [40]. Second, we qualitatively test the proposed approach on several different datasets collected utilizing a custom made sensor suite [16] and an Aqua2 AUV [17].…”

Section: Resultsmentioning

confidence: 99%

“…( 3), is taken only with the matched landmarks with loop candidate for calculating the sonar error term and reprojection error; see Eq. (16).…”

Section: Loop-closing and Relocalizationmentioning

confidence: 99%

“…Here, we show results from four different datasets in three different underwater environments. First, a sunken bus in Fantasy Lake (NC), where data was collected by a diver with a custommade underwater sensor suite [16]. The diver started from outside the bus, performed a loop around and entered in it from the back door, exited across and finished at the fronttop of the bus.…”

Section: B Underwater Datasetsmentioning

confidence: 99%

“…Second, we test the proposed full system on several different underwater datasets in a diverse set of conditions. More specifically, underwater data -consisting of visual, inertial, depth, and acoustic measurements -has been collected using a custom made sensor suite [16] from different locales; furthermore, data collected by an Aqua2 underwater vehicle [17] include visual, inertial, and depth measurements. The results on the underwater datasets illustrate the loss of tracking and/or failure to maintain consistent scale for other state-of-the-art systems while our proposed method maintains correct scale without diverging.…”

Section: Introductionmentioning

confidence: 99%

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SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

Rahman

Rekleitis

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain.Our previous work, SVIn, augmented the state-of-the-art visual-inertial state estimation package OKVIS to accommodate acoustic data from sonar in a non-linear optimization-based framework. This paper addresses drift and loss of localization -one of the main problems affecting other packages in underwater domain -by providing the following main contributions: a robust initialization method to refine scale using depth measurements, a fast preprocessing step to enhance the image quality, and a real-time loop-closing and relocalization method using bag of words (BoW). An additional contribution is the addition of depth measurements from a pressure sensor to the tightly-coupled optimization formulation. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle from challenging underwater environments with poor visibility demonstrate performance never achieved before in terms of accuracy and robustness.

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Section: Resultsmentioning

confidence: 99%

“…( 3), is taken only with the matched landmarks with loop candidate for calculating the sonar error term and reprojection error; see Eq. (16).…”

Section: Loop-closing and Relocalizationmentioning

confidence: 99%

Section: B Underwater Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SVIn2: An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor

Rahman

Rekleitis

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Dataset description. The experimental data were collected using a custom-made sensor suite (Rahman et al, 2018a) (see Figure 3) and an Aqua2 robot (Dudek et al, 2005). Both are equipped with a stereo camera, an IMU, and a pressure sensor.…”

Section: Validation On Underwater Datasetsmentioning

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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