• 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.

• ETRI Journal
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• v.36 no.6
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• pp.913-923
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• 2014

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi-robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional-vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.1-13
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• 2019

The advent of a fourth industrial revolution, built on advances in digital technology, has coincided with studies using various unmanned aerial vehicles (UAVs) being performed worldwide. However, the accuracy of different sensors and their suitability for particular research studies are factors that need to be carefully evaluated. In this study, we evaluated UAV photogrammetry using smart technology. To assess the performance of digital photogrammetry, the accuracy of common procedures for generating orthomosaic images and digital surface models (DSMs) using terrestrial laser scanning (TLS) techniques was measured. Two different type of non-surveying camera(Smartphone camera, fisheye camera) were attached to UAV platform. For fisheye camera, lens distortion was corrected by considering characteristics of lens. Accuracy of orthoimage and DSM generated were comparatively analyzed using aerial and TLS data. Accuracy comparison analysis proceeded as follows. First, we used Ortho mosaic image to compare the check point with a certain area. In addition, vertical errors of camera DSM were compared and analyzed based on TLS. In this study, we propose and evaluate the feasibility of UAV photogrammetry which can acquire 3 - D spatial information at low cost in a construction site.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.107-118
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• 2019

This paper deals with actual optical data from rural as well as urban areas in a nationwide study captured with Fisheye cameras. Simultaneously data was collected (of the receiving power density) from the mobile communications satellite N-STAR. The visibility of the satellite is easily determined by checking the value of the pixels in the binarized fisheye image of its position. The process of determining the visible satellite is automatically performed. Based on the analyses of the field data measured in Japan, we are expecting HEOs (Highly inclined Elliptical Orbiters) that would reduce blockage in the extreme northern region of Wakkanai City well as in the most crowded urban area, in Tokyo Ginza. In case of HEOs operation, the elevation angle will improve from 37 with N-STAR GEO to 75 degrees. HEOs could replace 5G/Ka-band or support in rural areas where broadband circuit is not available. We are proposing combination operations with HEOs and 5G/Ka-band to solve blockage problems, because HEOs can keep line-of-sight propagation with high elevation angle for long duration. In such operations, the communications profile on the vehicle based on actual optical data will be very useful to predict blockages and to select/switch a suitable circuit.

• 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 Korea Game Society
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• v.6 no.2
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• pp.45-50
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• 2006

In order to generate a photo-realistic synthesized image, we should reconstruct light environment by 3D analysis of scene. This paper presents a novel method for identifying the positions and characteristics of the lights-the global and local lights-in the real image, which are used to illuminate the synthetic objects. First, we generate High Dynamic Range(HDR) radiance map from omni-directional images taken by a digital camera with a fisheye lens. Then, the positions of the camera and light sources in the scene are identified automatically from the correspondences between images without a priori camera calibration. Types of the light sources are classified according to whether they illuminate the whole scene, and then we reconstruct 3D illumination environment. Experimental results showed that the proposed method with distributed ray tracing makes it possible to achieve photo-realistic image synthesis. It is expected that animators and lighting experts for the film and animation industry would benefit highly from it.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1283-1289
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• 2016

In this paper, we propose a new localization system using ceiling outlines. We acquire the entire ceiling image by using fisheye lens camera, and extract the lines by binarization and segmentation. The optical flow algorithm is then applied to identify the ceiling region from the segmented regions. Finally we obtain the position and orientation of the robot by the center position and momentum of ceiling region. Since we use the fully detected outlines, the accuracy and reliability of the localization system is improved. The experimental result are finally presented to show the effectiveness of the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.212-215
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• 2021

본 논문에서는 6 Fisheye lens 원본 영상에 대하여 Insta360 stitcher, AutoStitch[4], As-Projective-AsPossible(APAP)[5] 스티칭 방법으로 360 도 파노라마 영상을 생성하고 기하학적 왜곡과 컬러 왜곡을 비교 평가한다. 360 도 파노라마 Image Quality Assessment(IQA) 메트릭으로 Natural Image Quality Evaluator(NIQE)[6], Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)[7], Perception based Image Quality Evaluator(PIQE)[8], Feature Similarity(FSIM)[9] 그리고 high frequency feature 에 대한 Structural Similarity(SSIM)[10]을 측정하여 정량적 평가를 하며 정성적인 비교를 통하여 파노라마 영상의 품질과 평가 메트릭에 대한 벤치마크를 제공한다.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.

• Annual Conference of KIPS
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• 2017.04a
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• pp.1005-1007
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• 2017

본 논문에서는 어안렌즈를 통해 획득한 전방향 영상을 파노라마 영상으로 변환하기 위한 영역분할 방법을 제안한다. 각 분할된 영역에 대한 변환 과정에서 발생하는 영상 왜곡을 완화하면서 에지를 보존하기 위하여 기존 양성형 보간 방법을 대체하는 바이래터럴 필터 보간 방법을 제안한다. 또한 파노라마 변환 영상에서 본 논문의 제안방법의 결과가 기존 결과보다 에지가 잘 보존됨을 확인하였다.