Design and Implement of a 3D Object Recognition and Pose Estimation System Based on RGB-D Cameras

• Main Authors: Shu-Hsiang Tsai, 蔡述翔
• Other Authors: 蔡奇謚
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/20809383653674818640

碩士 === 淡江大學 === 電機工程學系碩士班 === 104 === Object recognition and pose estimation are important functions in applications of computer vision. In recent years, RGB-D cameras become more and more popular and 3D object recognition technology has got more and more attention as it not only has a higher object recognition rate in a complex environement, but also is able to accurately estimate the 3D pose information of the object in the workspace. Hence, this thesis presents a novel design of a RGB-D camera based 3D object recognition and pose estimation system. First of all, The proposed system takes colored point cloud data and extracts keypoints of the scene from the RGB-D camera. Then, the existing Color Signature of Histograms of Orientations (CSHOT) description algorithm is employed to build descriptors of the detected keypoints based on texture and shape information. Given the extractd keypoint descriptors, a 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 adopted to filter out matching errors in the correspondence set and estimate the initial 3D pose of the object. Finally, the pose estimation stage employs RANSAC and hypothesis verification algorithms to refine the initial pose and filter out poor estimation results with error hypothesis. Experimental results show that the proposed system not only successfully recognizes object in a complex scene, but also is able to accurately estimate the 3D pose information of the object with respect to the camera.