TY - GEN
T1 - Real-time 3D model-based tracking using edge and keypoint features for robotic manipulation
AU - Choi, Changhyun
AU - Christensen, Henrik I.
PY - 2010
Y1 - 2010
N2 - We propose a combined approach for 3D real-time object recognition and tracking, which is directly applicable to robotic manipulation. We use keypoints features for the initial pose estimation. This pose estimate serves as an initial estimate for edge-based tracking. The combination of these two complementary methods provides an efficient and robust tracking solution. The main contributions of this paper includes: 1) While most of the RAPID style tracking methods have used simplified CAD models or at least manually well designed models, our system can handle any form of polygon mesh model. To achieve the generality of object shapes, salient edges are automatically identified during an offline stage. Dull edges usually invisible in images are maintained as well for the cases when they constitute the object boundaries. 2) Our system provides a fully automatic recognition and tracking solution, unlike most of the previous edge-based tracking that require a manual pose initialization scheme. Since the edge-based tracking sometimes drift because of edge ambiguity, the proposed system monitors the tracking results and occasionally re-initialize when the tracking results are inconsistent. Experimental results demonstrate our system's efficiency as well as robustness.
AB - We propose a combined approach for 3D real-time object recognition and tracking, which is directly applicable to robotic manipulation. We use keypoints features for the initial pose estimation. This pose estimate serves as an initial estimate for edge-based tracking. The combination of these two complementary methods provides an efficient and robust tracking solution. The main contributions of this paper includes: 1) While most of the RAPID style tracking methods have used simplified CAD models or at least manually well designed models, our system can handle any form of polygon mesh model. To achieve the generality of object shapes, salient edges are automatically identified during an offline stage. Dull edges usually invisible in images are maintained as well for the cases when they constitute the object boundaries. 2) Our system provides a fully automatic recognition and tracking solution, unlike most of the previous edge-based tracking that require a manual pose initialization scheme. Since the edge-based tracking sometimes drift because of edge ambiguity, the proposed system monitors the tracking results and occasionally re-initialize when the tracking results are inconsistent. Experimental results demonstrate our system's efficiency as well as robustness.
UR - http://www.scopus.com/inward/record.url?scp=77955794017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955794017&partnerID=8YFLogxK
U2 - 10.1109/ROBOT.2010.5509171
DO - 10.1109/ROBOT.2010.5509171
M3 - Conference contribution
AN - SCOPUS:77955794017
SN - 9781424450381
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4048
EP - 4055
BT - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
T2 - 2010 IEEE International Conference on Robotics and Automation, ICRA 2010
Y2 - 3 May 2010 through 7 May 2010
ER -