• Journal of Digital Convergence
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• v.15 no.12
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• pp.543-548
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• 2017

$360^{\circ}$panoramic image can give people an unprecedented visual experience, and there are many different ways to make a $360^{\circ}$panoramic image. In this paper, we will introduce two easy and effective methods from those many ways. The first one is through 48 photos to make a $360^{\circ}$panoramic image, the second way is through 6 photos to make a $360^{\circ}$panoramic image. We will compare those methods and tell the audience which one suits themselves. Through those easy design methods introduced above, we can see VR works design became easy and popular, normal people can also make $360^{\circ}$panoramic image, and it promotes the industry of VR image contents.

• Korean Journal of Optics and Photonics
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• v.26 no.2
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• pp.88-97
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• 2015

An omnidirectional optical system can be described as a special optical system that images in real time a panoramic image with an azimuthal angle of $360^{\circ}$ and the altitude angle corresponding to the upper and lower fields of view from the horizon line. In this paper, for easy fabrication and compact size, we designed and fabricated a catadioptric omnidirectional optical system consisting of the mirror part of a spherical mirror with a central hole (that is, obscuration), a plane mirror, the imaging lens part of 3 single spherical lenses, and a spherical doublet in the visible light spectrum. We evaluated its image performance by measuring the cut-off spatial frequency using automobile license plates, and the vertical field of view using an ISO 12233 chart. We achieved a catadioptric omnidirectional optical system with vertical field of view from $+53^{\circ}$ to $-17^{\circ}$ and an azimuthal angle of $360^{\circ}$. This optical system cleaniy imaged letters on a car's front license plate at the object distance of 3 meters, which corresponds to a cut-off spatial frequency of 135 lp/mm.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.206-210
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• 2011

Vision-based robot localization is challenging due to the vast amount of visual information available, requiring extensive storage and processing time. To deal with these challenges, we propose the use of features extracted from omni-directional panoramic images and present a method for localization of a mobile robot equipped with an omni-directional camera. The core of the proposed scheme may be summarized as follows : First, we utilize an omni-directional camera which can capture instantaneous $360^{\circ}$ panoramic images around a robot. Second, Nodes around the robot are extracted by the correlation coefficients of Circular Horizontal Line between the landmark and the current captured image. Third, the robot position is determined from the locations by the proposed correlation-based landmark image matching. To accelerate computations, we have assigned the node candidates using color information and the correlation values are calculated based on Fast Fourier Transforms. Experiments show that the proposed method is effective in global localization of mobile robots and robust to lighting variations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.4
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• pp.517-524
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• 2007

This paper presents a global localization method using circular correlation of an omni-directional image. The localization of a mobile robot, especially in indoor conditions, is a key component in the development of useful service robots. Though stereo vision is widely used for localization, its performance is limited due to computational complexity and its narrow view angle. To compensate for these shortcomings, we utilize a single omni-directional camera which can capture instantaneous $360^{\circ}$ panoramic images around a robot. Nodes around a robot are extracted by the correlation coefficients of CHL (Circular Horizontal Line) between the landmark and the current captured image. After finding possible near nodes, the robot moves to the nearest node based on the correlation values and the positions of these nodes. To accelerate computation, correlation values are calculated based on Fast Fourier Transforms. Experimental results and performance in a real home environment have shown the feasibility of the method.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.8
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• pp.868-874
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• 2014

This paper proposes a novel mapping algorithm in Omni-directional Vision SLAM based on an obstacle's feature extraction using Lucas-Kanade Optical Flow motion detection and images obtained through fish-eye lenses mounted on robots. Omni-directional image sensors have distortion problems because they use a fish-eye lens or mirror, but it is possible in real time image processing for mobile robots because it measured all information around the robot at one time. In previous Omni-Directional Vision SLAM research, feature points in corrected fisheye images were used but the proposed algorithm corrected only the feature point of the obstacle. We obtained faster processing than previous systems through this process. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we remove the feature points of the floor surface using a histogram filter, and label the candidates of the obstacle extracted. Third, we estimate the location of obstacles based on motion vectors using LKOF. Finally, it estimates the robot position using an Extended Kalman Filter based on the obstacle position obtained by LKOF and creates a map. We will confirm the reliability of the mapping algorithm using motion estimation based on fisheye images through the comparison between maps obtained using the proposed algorithm and real maps.