• Title, Summary, Keyword: RGB-D

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Obstacle Avoidance of Indoor Mobile Robot using RGB-D Image Intensity (RGB-D 이미지 인텐시티를 이용한 실내 모바일 로봇 장애물 회피)

  • Kwon, Ki-Hyeon;Lee, Hyung-Bong
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.10
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    • pp.35-42
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    • 2014
  • It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.

Convenient View Calibration of Multiple RGB-D Cameras Using a Spherical Object (구형 물체를 이용한 다중 RGB-D 카메라의 간편한 시점보정)

  • Park, Soon-Yong;Choi, Sung-In
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.8
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    • pp.309-314
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    • 2014
  • To generate a complete 3D model from depth images of multiple RGB-D cameras, it is necessary to find 3D transformations between RGB-D cameras. This paper proposes a convenient view calibration technique using a spherical object. Conventional view calibration methods use either planar checkerboards or 3D objects with coded-pattern. In these conventional methods, detection and matching of pattern features and codes takes a significant time. In this paper, we propose a convenient view calibration method using both 3D depth and 2D texture images of a spherical object simultaneously. First, while moving the spherical object freely in the modeling space, depth and texture images of the object are acquired from all RGB-D camera simultaneously. Then, the external parameters of each RGB-D camera is calibrated so that the coordinates of the sphere center coincide in the world coordinate system.

High-quality Texture Extraction for Point Clouds Reconstructed from RGB-D Images (RGB-D 영상으로 복원한 점 집합을 위한 고화질 텍스쳐 추출)

  • Seo, Woong;Park, Sang Uk;Ihm, Insung
    • Journal of The Korea Computer Graphics Society
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    • v.24 no.3
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    • pp.61-71
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    • 2018
  • When triangular meshes are generated from the point clouds in global space reconstructed through camera pose estimation against captured RGB-D streams, the quality of the resulting meshes improves as more triangles are hired. However, for 3D reconstructed models beyond some size threshold, they become to suffer from the ugly-looking artefacts due to the insufficient precision of RGB-D sensors as well as significant burdens in memory requirement and rendering cost. In this paper, for the generation of 3D models appropriate for real-time applications, we propose an effective technique that extracts high-quality textures for moderate-sized meshes from the captured colors associated with the reconstructed point sets. In particular, we show that via a simple method based on the mapping between the 3D global space resulting from the camera pose estimation and the 2D texture space, textures can be generated effectively for the 3D models reconstructed from captured RGB-D image streams.

Real-time 3D Volumetric Model Generation using Multiview RGB-D Camera (다시점 RGB-D 카메라를 이용한 실시간 3차원 체적 모델의 생성)

  • Kim, Kyung-Jin;Park, Byung-Seo;Kim, Dong-Wook;Kwon, Soon-Chul;Seo, Young-Ho
    • Journal of Broadcast Engineering
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    • v.25 no.3
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    • pp.439-448
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    • 2020
  • In this paper, we propose a modified optimization algorithm for point cloud matching of multi-view RGB-D cameras. In general, in the computer vision field, it is very important to accurately estimate the position of the camera. The 3D model generation methods proposed in the previous research require a large number of cameras or expensive 3D cameras. Also, the methods of obtaining the external parameters of the camera through the 2D image have a large error. In this paper, we propose a matching technique for generating a 3D point cloud and mesh model that can provide omnidirectional free viewpoint using 8 low-cost RGB-D cameras. We propose a method that uses a depth map-based function optimization method with RGB images and obtains coordinate transformation parameters that can generate a high-quality 3D model without obtaining initial parameters.

Point Cloud Registration Algorithm Based on RGB-D Camera for Shooting Volumetric Objects (체적형 객체 촬영을 위한 RGB-D 카메라 기반의 포인트 클라우드 정합 알고리즘)

  • Kim, Kyung-Jin;Park, Byung-Seo;Kim, Dong-Wook;Seo, Young-Ho
    • Journal of Broadcast Engineering
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    • v.24 no.5
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    • pp.765-774
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    • 2019
  • In this paper, we propose a point cloud matching algorithm for multiple RGB-D cameras. In general, computer vision is concerned with the problem of precisely estimating camera position. Existing 3D model generation methods require a large number of cameras or expensive 3D cameras. In addition, the conventional method of obtaining the camera external parameters through the two-dimensional image has a large estimation error. In this paper, we propose a method to obtain coordinate transformation parameters with an error within a valid range by using depth image and function optimization method to generate omni-directional three-dimensional model using 8 low-cost RGB-D cameras.

Real-time Full-view 3D Human Reconstruction using Multiple RGB-D Cameras

  • Yoon, Bumsik;Choi, Kunwoo;Ra, Moonsu;Kim, Whoi-Yul
    • IEIE Transactions on Smart Processing and Computing
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    • v.4 no.4
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    • pp.224-230
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    • 2015
  • This manuscript presents a real-time solution for 3D human body reconstruction with multiple RGB-D cameras. The proposed system uses four consumer RGB/Depth (RGB-D) cameras, each located at approximately $90^{\circ}$ from the next camera around a freely moving human body. A single mesh is constructed from the captured point clouds by iteratively removing the estimated overlapping regions from the boundary. A cell-based mesh construction algorithm is developed, recovering the 3D shape from various conditions, considering the direction of the camera and the mesh boundary. The proposed algorithm also allows problematic holes and/or occluded regions to be recovered from another view. Finally, calibrated RGB data is merged with the constructed mesh so it can be viewed from an arbitrary direction. The proposed algorithm is implemented with general-purpose computation on graphics processing unit (GPGPU) for real-time processing owing to its suitability for parallel processing.

Recent Trends of Real-time 3D Reconstruction Technology using RGB-D Cameras (RGB-D 카메라 기반 실시간 3차원 복원기술 동향)

  • Kim, Y.H.;Park, J.Y.;Lee, J.S.
    • Electronics and Telecommunications Trends
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    • v.31 no.4
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    • pp.36-43
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    • 2016
  • 실 환경에 존재하는 모든 것을 3차원 모델로 쉽게 복원할 수 있을 것이라는 생각과 원격지에 있는 환경과 사람을 같은 공간에 있는 듯 상호작용할 수 있게 된 것은 그리 오래되지 않았다. 이는 일정 해상도를 보장해주는 RGB-D 센서의 개발과 이러한 센서들을 사용한 3차원 복원 관련 연구들이 활발히 수행되면서 가능하게 되었다. 본고에서는 널리 쓰이고 있는 RGB-D 카메라를 사용하여 실시간으로 때로는 온라인상에서 3차원으로 복원하고 가시화하는 기술에 대하여 살펴보고자 한다. 하나 또는 여러 개의 RGB_D 카메라를 사용하거나 모바일 장치에 장착된 RGB-D 센서를 사용하여 넓은 공간, 움직이는 사람, 온라인 상태의 환경 등을 실시간으로 복원하기 위한 기술들을 세부적으로 설명한다. 또한, 최근에 발표된 기술들이 다루고 있는 이슈들을 설명하고 향후 3차원 복원기술의 연구개발 방향에 대해서 논의한다.

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Image Feature-Based Real-Time RGB-D 3D SLAM with GPU Acceleration (GPU 가속화를 통한 이미지 특징점 기반 RGB-D 3차원 SLAM)

  • Lee, Donghwa;Kim, Hyongjin;Myung, Hyun
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.5
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    • pp.457-461
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    • 2013
  • This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

A Robot Localization based on RGB-D Sensor (RGB-D 센서 기반의 로봇 위치추정 기법 연구)

  • Seo, Yu-Hyeon;Lee, Hyun
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.872-875
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    • 2014
  • 재난방지 및 구호에 사용되는 로봇의 주된 목적은 인간이 직접적인 접근을 할 수 없는 지역을 사전에 탐사하여 인간으로 하여금 올바른 판단을 돕기 위함에 있다. 하지만, 재난 지역에서는 통신장애 문제나, 육안 식별이 불가능한 상황, 원격조정을 통하여 로봇이 업무 수행에 상당한 제약을 받는다. 이 문제를 해결하기 위해 "LED-RGB 칼라센서를 이용한 상호위치 인식 방법연구"[1]을 수행하였으나, RGB의 인식거리가 상당히 짧고, 판단이 모호한 단점이 발생하였다. 따라서 본 연구에서는 이를 개선한 RGB-D센서를 이용하여 RGB의 인식거리를 증가시켰다. 또한 더욱 높은 정확성을 이용하기 위해, Depth를 사용하여 사물들의 특징점들을 랜드마크로 하고 랜드마크로부터의 상대위치를 파악하여 위치를 추정하는 방법을 제안하고자 한다. 마지막으로 상호인식 알고리즘을 이전 방식과 비교하고자 한다.

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Educational Indoor Autonomous Mobile Robot System Using a LiDAR and a RGB-D Camera (라이다와 RGB-D 카메라를 이용하는 교육용 실내 자율 주행 로봇 시스템)

  • Lee, Soo-Young;Kim, Jae-Young;Cho, Se-Hyoung;Shin, Chang-yong
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.44-52
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    • 2019
  • We implement an educational indoor autonomous mobile robot system that integrates LiDAR sensing information with RGB-D camera image information and exploits the integrated information. This system uses the existing sensing method employing a LiDAR with a small number of scan channels to acquire LiDAR sensing information. To remedy the weakness of the existing LiDAR sensing method, we propose the 3D structure recognition technique using depth images from a RGB-D camera and the deep learning based object recognition algorithm and apply the proposed technique to the system.