Survey on Camera Calibration Technique

Song, Liming; Wu, Wenfu; Guo, Junrong; Li, Xiuhua

doi:10.1109/ihmsc.2013.240

Cited by 38 publications

(18 citation statements)

References 3 publications

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“…An artificial landmark could be any object whether static or mobile which could vary in size, shape, feature or color as long as it is placed in the environment with the purpose of robot localization. The author in [73] use a sticker and LED array as an artificial landmark. These makers are easier to detect and describe because the details of the objects used are known in advance.…”

Section: Landmarksmentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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Section: Landmarksmentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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“…In the field of computer vision, many methods have been developed for determining camera parameters from photographs [3][4][5][6]. However, these methods focus on images of ordinary objects such as buildings or landscapes, and are unsuitable for images of starry skies because their characteristics are completely different from those of ordinary pictures.…”

Section: Introductionmentioning

confidence: 99%

Estimating camera parameters from starry night photographs

Ishikawa

Dobashi

2020

Comp. Visual Media

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We propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes (i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.

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“…The corresponding geometric parameters are camera parameters and obtained by camera calibration [3,8,26]. The camera parameters can be divided into internal and external parameters.…”

Section: Calibration Parameters Descriptionmentioning

confidence: 99%

“…It is divided into either linear model or nonlinear model [1,3,8]. The linear model is based on the principle of pinhole imaging, and its geometric relationship is established between the image points and the corresponding object space points.…”

Section: Camera Calibration Modelmentioning

confidence: 99%

See 1 more Smart Citation

A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

Deng

Shao

et al. 2016

Neurocomputing

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A novel camera calibration technique based on differential evolution particle swarm optimization algorithm, Neurocomputing, http://dx.doi.org/10.1016/j.neucom. 2015.03.119 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractCamera calibration is one of the fundamental issues in computer vision and aims at determining the intrinsic and exterior camera parameters by using image features and the corresponding 3D features. This paper proposes a relationship model for camera calibration in which the geometric parameter and the lens distortion effect of camera are taken into account in order to unify the world coordinate system (WCS), the camera coordinate system (CCS) and the image coordinate system (ICS). Differential evolution is combined with particle swarm optimization algorithm to calibrate the camera parameters effectively. Experimental results show that the proposed algorithm has a good optimization ability to avoid local optimum and can complete the visual identification tasks accurately.

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Survey on Camera Calibration Technique

Cited by 38 publications

References 3 publications

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

Estimating camera parameters from starry night photographs

A novel camera calibration technique based on differential evolution particle swarm optimization algorithm

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