TY - GEN
T1 - Feature Matching Algorithm Design and Verification in Rotates Camera Normal Region Based on ROS System
AU - Mi, Ying
AU - Yuan, Shihua
AU - Li, Xueyuan
AU - Zhou, Junjie
AU - Yin, Xufeng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - Intelligent vehicle is one kind of popular research field at present, and the environment perception of unmanned vehicle, especially the detection and tracking of target objects, is indispensable for it's development. In this paper, we built an image acquisition simulation environment based on the robot operating system (ROS) and Gazebo, and designed a algorithm to realize the feature matching in normal region of the rotating camera. Different from the common matching algorithm, this paper places the target object at the imaging center of the right camera through parameter setting and rotates the left camera in the normal ROI region to obtain the images of the two cameras for feature matching. In order to verify the accuracy of the algorithm, during the rotation of left camera this paper selected the symmetrical angle with the camera on the right side, as well as 10 angles for the change of the fixed step size on the left and right sides of the symmetrical angle to acquire images, and analyzed and compared the angles and matching rate as parameters to verify the accuracy and stability of the matching algorithm.
AB - Intelligent vehicle is one kind of popular research field at present, and the environment perception of unmanned vehicle, especially the detection and tracking of target objects, is indispensable for it's development. In this paper, we built an image acquisition simulation environment based on the robot operating system (ROS) and Gazebo, and designed a algorithm to realize the feature matching in normal region of the rotating camera. Different from the common matching algorithm, this paper places the target object at the imaging center of the right camera through parameter setting and rotates the left camera in the normal ROI region to obtain the images of the two cameras for feature matching. In order to verify the accuracy of the algorithm, during the rotation of left camera this paper selected the symmetrical angle with the camera on the right side, as well as 10 angles for the change of the fixed step size on the left and right sides of the symmetrical angle to acquire images, and analyzed and compared the angles and matching rate as parameters to verify the accuracy and stability of the matching algorithm.
KW - ROS System
KW - Real-time Image Matching
KW - Unmanned ground Vehicle
UR - https://www.scopus.com/pages/publications/85072392913
U2 - 10.1109/ICMA.2019.8816639
DO - 10.1109/ICMA.2019.8816639
M3 - Conference contribution
AN - SCOPUS:85072392913
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 342
EP - 347
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
Y2 - 4 August 2019 through 7 August 2019
ER -