Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios

DeMarco, Kevin J.; West, M. E.; Howard, Ayanna M.

doi:10.1109/smc.2013.406

Cited by 41 publications

(28 citation statements)

References 9 publications

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“…A similar result could be obtained by eroding and dilating the white areas after binarization, as performed in [10]. After blurring, binarization of the image is performed with adaptive thresholding.…”

Section: 21mentioning

confidence: 79%

“…Parameter ] denotes measurement noise which is, in this case, modeled as zero mean Gaussian noise. Note that bearing measurement is relative; therefore, there is a heading state included in (10).…”

Section: Tracking Filtermentioning

confidence: 99%

“…In [9] authors provide algorithms for detection of man-made objects on sea floor, where they mostly focus on target-seabed separation issue. The most similar attempt to our work was done in [10], where the sonar was used to detect a human diver. The authors used a similar image processing approach as us, followed by a hidden Markov model-based algorithm for candidate classification.…”

Section: Introductionmentioning

confidence: 97%

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Underwater Object Tracking Using Sonar and USBL Measurements

et al. 2016

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In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.

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Section: 21mentioning

confidence: 79%

Section: Tracking Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Underwater Object Tracking Using Sonar and USBL Measurements

et al. 2016

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“…for 1 ≤ j ≤ N, where pc(j) is the row pixel index of the extended highlight in the j th column of the sonar image. Finally, the authors extracted extended highlight in the ineffective region by applying a difference filter D such as (6) and selecting pixels that exceed a threshold value.…”

Section: D-data-calculation-based Object Detectionmentioning

confidence: 99%

“…NDERWATER object detection is necessary for autonomous underwater vehicles (AUVs) to accomplish various underwater missions [1]- [6]. Furthermore, forward scan sonar (FSS) is one of the widely used sensors in underwater operations [7]- [10].…”

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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Understanding human motion and gestures for underwater human–robot collaboration

Islam

Sattar³

2018

Journal of Field Robotics

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In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatial-and frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closed-and open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods.

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Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios

Cited by 41 publications

References 9 publications

Underwater Object Tracking Using Sonar and USBL Measurements

Underwater Object Tracking Using Sonar and USBL Measurements

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

Understanding human motion and gestures for underwater human–robot collaboration

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