Visual measurement and control for underwater robots: A survey

Sun, Feihu; Yu, Junzhi; Xu, De

doi:10.1109/ccdc.2013.6560944

Cited by 19 publications

(6 citation statements)

References 22 publications

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“…Before solving T C1 S1 , the camera intrinsic parameters must be known, which can be calculated by a MATLAB calibration toolbox developed based on the Zhang's calibration method [22,23]. If the coordinates of each feature point in the world coordinate system (target coordinate system) and the image coordinates of the corresponding feature points are known, the homogeneous transformation T C1 S1 between the camera coordinate system and target coordinate system can be calculated by the P3P algorithm [24,25].…”

Section: And the X Axis Is Along The Vectormentioning

confidence: 99%

A multi-node synchronous baseline calibration system based on vision measurement for DPOS

Liu¹,

Wen²,

Bo³

et al. 2019

Meas. Sci. Technol.

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A distributed position and orientation system (DPOS) can provide abundant time-spatial information for multi-task imaging loads in the airborne earth observation system. To ensure that DPOS meets the engineering requirement, it must be accurately calibrated on the ground before DPOS can be applied to aerial remote sensing, especially baseline calibration, for which the measurement accuracy of the baseline is a key technical indicator that affects imaging accuracy. However, it is difficult to achieve baseline calibration between multiple nodes synchronously because of external interference, and there is no baseline calibration method for DPOS yet. To solve the problem mentioned above, considering the advantages of vision measurement, this paper presents a multi-node synchronous baseline calibration system for DPOS. In order to evaluate the effectiveness of the proposed system, a verification test was conducted. The measurement result of a theodolite coordinate measurement system (TCMS) is the benchmark. Under static conditions, the experimental results show that baseline measurement accuracy is better than 0.3 mm.

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Section: And the X Axis Is Along The Vectormentioning

confidence: 99%

A multi-node synchronous baseline calibration system based on vision measurement for DPOS

Liu¹,

Wen²,

Bo³

et al. 2019

Meas. Sci. Technol.

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“…The aim is to develop a robust system that can work with underwater imagery, which due to light backscatter in water, presents non-uniform illumination and image degradation (poor contrast and distortion). A list of these computer vision challenges, both in hardware and software, is presented in [14]. For such conditions, tracking algorithms based on ensemble methods and discriminant classifiers [15] [16] show the best state of the art accuracy and noise-insensitivity.…”

Section: Introductionmentioning

confidence: 99%

Visual diver detection using multi-descriptor nearest-class-mean random forests in the context of underwater Human Robot Interaction (HRI)

Chavez

Pfingsthorn

Birk

et al. 2015

OCEANS 2015 - Genova

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This paper introduces a visual method for diver detection in the context of Human Robot Interaction (HRI). The detection is treated as a classification problem, where a discriminative model is trained by computing image features of the target (diver) and underwater scenery. This type of scenery poses great challenges due to its high variability, as it often presents high illumination changes, scarce features and image distortions. For this reason, it is desirable to represent this type of images with multiple type of complementary features. The system scalability is, however, lowered as the number of features types increase as the amount of data to represent queries and indexes also increases.To remedy this, we modified the Nearest Class Mean Forests (NCMF) method, a variant of Random Forests, to integrate as many features types as desired without concerning about scalability and performance decay. The system outperforms the common generative tracking methods which fail to encompass different type of distortions into one model and ignore background information. And in contrast to tracking methods using acoustic sensors which output a single value (distance to the diver), our approach outputs a region encompassing the diver's body; information that can be further exploited to enhance underwater HRI. Not to mention that camera setups offer higher flexibility in size and energy consumption constraints than acoustic sensors. All of the system's aforementioned capabilities are tested with real-life data obtained from field experiments.978-1-4799-8736-8/15/$31.00 ©2015 IEEE

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“…With the development of marine economy and technology, unmanned underwater vehicle (UUV) is an effective means for marine detection, resource exploitation, military interfere, and investigation [17][18][19]. Navigation of UUV has been and remains a substantial challenge to platforms.…”

Section: Introductionmentioning

confidence: 99%

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

Yan

Deng

et al. 2014

Mathematical Problems in Engineering

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A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponential inertia weight particle swarm optimization(EPSO), andtime-varying acceleration coefficient(TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

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Visual measurement and control for underwater robots: A survey

Cited by 19 publications

References 22 publications

A multi-node synchronous baseline calibration system based on vision measurement for DPOS

A multi-node synchronous baseline calibration system based on vision measurement for DPOS

Visual diver detection using multi-descriptor nearest-class-mean random forests in the context of underwater Human Robot Interaction (HRI)

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

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