Proc. SPIE. 8409, Third International Conference on Smart Materials and Nanotechnology in Engineering
KEYWORDS: Visual process modeling, Detection and tracking algorithms, Cameras, Error analysis, Electronic filtering, Algorithm development, Robot vision, Systems modeling, Process modeling, Affine motion model
This study deals with the development of two vision estimation algorithms for robot vision control scheme. One is the
Extended Kalman Filtering algorithm, and the other is the Newton-Raphson algorithm.
The Newton-Raphson (N-R) algorithm consists of vision system model, camera parameters estimation scheme and joint
angle estimation scheme. The Extended Kalman Filtering (EKF) algorithm consists of vision system model, process
model and measurement model. In addition, the process and the measurement models include the camera parameters
estimation scheme and the joint angle estimation scheme, respectively. The vision system model includes six camera
internal and external parameters.
Each algorithm has its strengths and weaknesses. The Newton-Raphson algorithm is based on iterations and can
concurrently handle large amounts of data. On the other hand, it takes a lot of processing time and accordingly is not
easy use for real-time robot control. The Extended Kalman Filtering algorithm is based on recursion and thus is faster,
but it requires very accurate selection of initial values. In this study we use Monte-Carlo method for estimating initial
Finally, the results of both algorithms are compared experimentally by tracking the moving target.