期刊論文
| 學年 | 106 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2018-02-20 |
| 作品名稱 | Simultaneous 3D Object Recognition and Pose Estimation Based on RGB-D Images |
| 作品名稱（其他語言） | |
| 著者 | Chi-Yi Tsai; Shu-Hsiang Tsai |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Access 6, p.28859-28869 |
| 摘要 | Object recognition and pose estimation are essential functions in applications of computer vision, and they also are fundamental modules in robotic vision systems. In recent years, RGB-D cameras become more and more popular, and the 3D object recognition technology has got more and more attention. In this paper, a novel design of simultaneous 3D object recognition and pose estimation algorithm is proposed based on RGB-D images. The proposed system converts the input RGB-D image to colored point cloud data and extracts features of the scene from the colored point cloud. Then, the existing color signature of histograms of orientations (CSHOT) description algorithm is employed to build descriptors of the detected features based on local texture and shape information. Given the extracted feature descriptors, a two-stage matching process is performed to find correspondences between the scene and a colored point cloud model of an object. Next, a Hough voting algorithm is used to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Finally, the pose estimation stage employs RANdom SAmple Consensus (RANSAC) and hypothesis verification algorithms to refine the initial pose and filter out poor estimation results with error hypotheses. Experimental results show that the proposed system not only successfully recognizes the object in a complex scene but also accurately estimates the 3D pose information of the object with respect to the camera. |
| 關鍵字 | |
| 語言 | en_US |
| ISSN | 2169-3536 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI Scopus |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115540 ) |