Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition and pose estimation using color cooccurrence histograms and geometric modeling
Image and Vision Computing
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A strategy for grasping unknown objects based on co-planarity and colour information
Robotics and Autonomous Systems
Object recognition and ontology for manipulation with an assistant robot
ICOST'10 Proceedings of the Aging friendly technology for health and independence, and 8th international conference on Smart homes and health telematics
Fuzzy automata system with application to target recognition based on image processing
Computers & Mathematics with Applications
The MOPED framework: Object recognition and pose estimation for manipulation
International Journal of Robotics Research
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
Computers & Mathematics with Applications
| Hi-index | 0.09 |
Intelligent grasping is still a hard problem for home service robots. There are two major issues in the intelligent grasping, i.e. the object recognition and the pose estimation. To grasp casually placed objects, the robot needs the object's full 6 degrees of freedom pose data. To deal with the challenges such as illumination changes, cluttered background, occlusion, etc., we propose a monocular vision based object recognition and 6D pose estimation method. The SIFT feature point matching and brute-force search algorithm is used to do a tentative object recognition. The object recognition result is then verified with the homography constraint. After passing the verification, the 6D pose estimation is obtained through the decomposition of the homography matrix and the result is refined using the Levenberg-Marquardt algorithm. We embed our pose estimation method in a tracking by detection framework to keep computing and refining the pose during the whole approaching procedure. To test our method, a robot arm of seven degrees of freedom was utilized for a group of grasping experiments. The experimental results showed that our approach successfully recognized and grasped a variety of household objects with decent accuracy.