Reliable pose estimation of underwater dock using single camera: a scene invariant approach

Ghosh, Sumit; Ray, R.K.; Vadali, Siva Ram Krishna; Shome, Sankar Nath; Nandy, Sambhunath

doi:10.1007/s00138-015-0736-4

Cited by 46 publications

(30 citation statements)

References 20 publications

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“…b a , the ratio of the semi-minor axis of length to the semi-major axis of length of the projection ellipse in one image, should be as small as possible to decrease the computation error of the spatial circle's normal vector n. b a in the other image should be near to 1 to decrease the computation error of radius r. And the angle between the two rays that connect the spatial circle's center with the optical center of the camera at two positions respectively should be large enough to decrease the computation error of the spatial circle's center coordinates X c . hand system with the proposed active movement strategy, the movement strategy in the study by Ghosh et al, 25 and the random movement strategy, respectively.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…The circular flat washer has one inner circle and one outer circle, as shown in Figure 6(b). Twenty experiments have been done with the proposed active movement strategy, the random movement strategy, and the movement strategy in the study by Ghosh et al, 25 respectively. Three images taken at Location 1, Location 2, and Location 3 are shown in Figure 10.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…The estimation error with the active movement strategy is smaller than that of the random movement and the movement strategy. 25 The movement of the monocular eye-in-hand system is planned in order to obtain the images from special perspectives. With the images from special perspectives, 6-D position-pose estimation of the nut can be improved.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Aiming at the application of autonomous underwater vehicle (AUV) docking in a circular-shaped docking station, the fact that the eccentricity of an ellipse monotonically decreases with decreasing view angle is exploited for pose estimation of the circular docking station. 25 The eccentricity of the ellipse from progressively captured images is computed and the AUV moves in the direction such that eccentricity ! 0 and hence ellipses tends to a circle.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Active 6-D position-pose estimation of a spatial circle using monocular eye-in-hand system

Feng

et al. 2018

International Journal of Advanced Robotic Systems

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Nuts and bolts are common components in assembly lines. Their position and pose estimation is a vital step for automatic assembling. Although many approaches using a monocular camera have been proposed, few works consider a monocular camera's active movements for improving estimation accuracy. This article presents an active movement strategy for a monocular eye-in-hand camera for high position and pose estimation accuracy of a spatial circle. Extensive experiments are conducted to validate the effectiveness of the proposed method for position and pose estimation of circles printed on paper, real circular flat washers, and nuts.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Active 6-D position-pose estimation of a spatial circle using monocular eye-in-hand system

Feng

et al. 2018

International Journal of Advanced Robotic Systems

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“…Optical sensors are usually responsible for the final short-distance stage precise docking due to its high accuracy and are often combined with other sensors which are responsible for long-distance rough direction [4]. A docking algorithm proposed using optical sensors consists of detection and pose estimation in [5]. Many papers report their results on pose estimation while less study is conducted on the detection.…”

Section: Introductionmentioning

confidence: 99%

Learning Deep Representations and Detection of Docking Stations Using Underwater Imaging

Liu

Özay

Okatani

et al. 2018

2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO)

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Underwater docking endows AUVs with the ability of recharging and data transfer. Detection of underwater docking stations is a crucial step required to perform a successful docking. We propose a method to detect underwater docking stations using two dimensional images captured under different environmental light variance, deformations aroused by scale and rotation, different light intensity and partial observation. In order to realize our proposed method, we first train Convolutional Neural Networks (CNNs) to learn feature representations and then employ a deep detection network. In order to analyze the performance of the proposed method, we prepared an image dataset of docking stations using underwater imaging. Then, we explore the performance of our method using different data augmentation methods. We improved the AUC of detection by 0.14 using data augmentation and obtained 0.88 AUC with data augmentation. An increment of 0.23 AUC is gained by transfer learning and we obtained 0.88 AUC on another datasets.

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Adaptive neural network control for visual docking of an autonomous underwater vehicle using command filtered backstepping

Zhang

Gao

Chen

et al. 2022

Intl J Robust & Nonlinear

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This article proposes an unscented Kalman filter-based visual docking controller for underactuated underwater vehicles using a position-based visual servoing (PBVS) approach. The relative pose of an underwater vehicle with respect to a moving docking station is estimated by an unscented Kalman filter with the visual measurements of multiple point features installed on the station. Based on the estimated pose, the Euler angles commands are designed via an integral cross-tracking docking method to drive the underwater vehicle to move along the desired docking path. Then, an adaptive neural network (NN) controller is designed to track the desired yaw and pitch angles using command filtered backstepping, in which a single-hidden-layer (SHL) neural network is employed to compensate for dynamic uncertainties and external disturbances. A barrier Lyapunov function is defined to improve the stability of tracking errors under attitude constraints to ensure the features are in the field of view, and hyperbolic tangent functions are utilized to deal with input saturation. Simulation studies and pool experiments are provided to demonstrate the performances of the proposed visual docking controller.

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Reliable pose estimation of underwater dock using single camera: a scene invariant approach

Cited by 46 publications

References 20 publications

Active 6-D position-pose estimation of a spatial circle using monocular eye-in-hand system

Active 6-D position-pose estimation of a spatial circle using monocular eye-in-hand system

Learning Deep Representations and Detection of Docking Stations Using Underwater Imaging

Adaptive neural network control for visual docking of an autonomous underwater vehicle using command filtered backstepping

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