• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.23-31
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• 2010

Due to the fact that fisheye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Not only use the camera as a viewing system, but also as a camera sensor, camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. In this thesis, we introduce a geometric correction technique to minimize the loss of the image data from a vehicle fish-eye lens having a field of view over $180^{\circ}$, and a asymmetric distortion. Geometric correction is a process in which a camera model with a distortion model is established, and then a corrected view is generated after camera parameters are calculated through a calibration process. First, the FOV model to imitate a asymmetric distortion configuration is used as the distortion model. Then, we need to unify the axis ratio because a horizontal view of the vehicle fish-eye lens is asymmetrically wide for the driver, and estimate the parameters by applying a non-linear optimization algorithm. Finally, we create a corrected view by a backward mapping, and provide a function to optimize the ratio for the horizontal and vertical axes. This minimizes image data loss and improves the visual perception when the input image is undistorted through a perspective projection.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.601-605
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• 2009

Due to the fact that fish-eye lens can provide super wide angles with the minimum number of cameras, field-of-view over 180 degrees, many vehicles are attempting to mount the camera system. Camera calibration should be preceded, and geometrical correction on the radial distortion is needed to provide the images for the driver's assistance. However, vehicle fish-eye cameras have diagonal output images rather than circular images and have asymmetric distortion beyond the horizontal angle. In this paper, we introduce a camera model and metric calibration method for vehicle cameras which uses feature points of the image. And undistort the input image through a perspective projection, where straight lines should appear straight. The method fitted vehicle fish-eye lens with different field of views.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.256-262
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• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1270-1275
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• 2013

A lot of research about camera calibration and lens distortion for wide-angle lens has been made. Especially, calibration for fish-eye lens which has 180 degree FOV(field of view) or above is more tricky, so existing research employed a huge calibration pattern or even 3D pattern. And it is important that calibration parameters (such as distortion coefficients) are suitably initialized to get accurate calibration results. It can be achieved by using manufacturer information or lease-square method for relatively narrow FOV(135, 150 degree) lens. In this paper, without any previous manufacturer information, camera calibration and barrel undistortion for fish-eye lens with over 180 degree FOV are achieved by only using one calibration pattern image. We applied QR decomposition for initialization and Regularization for optimization. With the result of experiment, we verified that our algorithm can achieve camera calibration and image undistortion successfully.

• 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.

• Journal of the Korean housing association
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• v.14 no.5
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• pp.37-46
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• 2003

The quantitative analysis of view tells how surroundings and sky are showed, and requires understanding of visual perception and three dimensional information of buildings. The visual perception and the existing projection methods for view analysis are examined. The results of this study are as follows: The visual perception on the size is determined by the visual angle, which can be described as a solid angle. The analysis of view by planar projection can be narrow-sighted according to the size of the window and the location of the viewpoint, which will cause the obstacles in the normal direction of the window interfere the view. For the analysis of view by fisheye projection, the area around the focus point is calculated wider than other areas, and so the view ratio depends on the position of the focus point. When analyzing sky view by dividing the sky vault into the differential area, the distortion by projection can be minimized.

• 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 the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.114-123
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• 2014

Vehicle Black Box (Event Data Recorder EDR) only recognizes the general surrounding environments of load. In addition, general EDR is difficult to recognize the images of a sudden illumination change. It appears that the lens is being a severe distortion. Therefore, general EDR does not provide the clues of the circumstances of the accident. To solve this problem, we estimate the value of Normalized Luminance Descriptor(NLD) and Normalized Contrast Descriptor(NCD). Illumination change is corrected using Normalized Image Quality(NIQ). Second, we are corrected lens distortion using model of Field Of View(FOV) based on designed method of fisheye lens. As a result, we propose integration algorithm of two methods that correct distortions of images using each Gamma Correction and Lens Correction in parallel.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.49-60
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• 2012

In this paper, we propose a hardware design for correction of barrel distortion using the nearest coordinates in the corrected image. Because it applies the nearest distance on corrected image rather than adjacent distance on distorted image, the picture quality is improved by the image whole area, solve the staircase phenomenon in the exterior area. But, because of additional arithmetic operation using design of bilinear interpolation, required arithmetic operation is increased. Look up table(LUT) structure is proposed in order to solve this, coordinate rotation digital computer(CORDIC) algorithm is applied. The results of the synthesis using Design compiler, the design of implementing all processes of the interpolation method with the hardware is higher than the previous design about the throughput, In case of the rear camera, the design of using LUT and hardware together can reduce the size than the design of implementing all processes with the hardware.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.7-18
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• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.