Camera Calibration: Transformation Real-World Coordinates into Camera Coordinates Using Neural Network

Abstract

Vision system will make robotic system has the ability to see and modeled the real world objects. There are many factors that can affect the process of robot vision such as lens distortion, camera position which is not always at the center on the robot environment, the robot and other objects movement. In this research, we design an architecture using neural network to apply for global vision in autonomous mobile robot engine. The scheme is concerning to the development of camera calibration technique using neural network for precise and accurate position and orientation the robots. Its goal is to develop a robust camera calibration technique, to estimate the parameters of a transformation in the real world coordinate into image coordinate systems in autonomous mobile robots. The objective of our research is to propose and develop calibration techniques in a global overhead vision system for autonomous mobile robots. It aims to map and identify the identity of a robot in various conditions and camera position. Artificial Neural Network method (ANN) has been proposed as a method for solving coordinates transformation problems for non-linear lens distortion. The coordinate transformation was tested by placing cameras at various heights and setting camera angle with various zoom and focal length values. © Springer-Verlag Berlin Heidelberg 2013.