3D Object Recognition and Pose Estimation
Date Issued
2012
Date
2012
Author(s)
Chen, Chia-Hung
Abstract
Recently, robotic technologies, from industrial machines to commercial entertainment products, are increasingly influential in our lives. There is continual development of robots for domestic, medical, and industrial purposes under way in corporate and university research labs. In efforts to make robots more intelligent and cognitive, robots have been developed to obtain much useful information including scene understanding and spatial relationship from a machine vision system.
The objective of this dissertation is to develop recognition methods for face recognition and pose estimation algorithms for autonomous grasping. The proposed face recognition method utilizes AAM to extract facial feature points and utilizes shape descriptors to recognize a face. Also, we demonstrate that the proposed pose estimation algorithm is capable of accurately computing an object’s pose by the 2D tracking points on an object of SIFT and 3D point cloud detected by stereo vision on an object, assuming that a 3D geometric model of an object is known a priori. Moreover, the visual guide framework integrating object detection, object localization, pose estimation, path planning and the real robot arm for guiding the robot arm to the target is established.
Finally, we demonstrate two grasping scenarios with a dexterous arm, ADAM, where an object in front of ADAM can be grasped. This demonstration shows our robot arm can robustly and autonomously grasp a randomly rotative rigid object detected by SIFT in 3D space.
Subjects
Face Recognition
Pose Estimation
Autonomous Grasping
Path Planning
Object Localization
Type
thesis
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