Document Details

Document Type : Article In Journal 
Document Title :
Robust Camera Calibration for the MiroSot and the AndroSot Vision Systems Using Artificial Neural Networks
Robust Camera Calibration for the MiroSot and the AndroSot Vision Systems Using Artificial Neural Networks
 
Subject : Computer Science 
Document Language : English 
Abstract : The MirosSot and the AndroSot soccer robots have the ability to recognize, and navigate within, their environments without human intervention. An overhead global camera, usually at a fixed position, is used for the robot’s vision. Because of the lens distortion, images obtained from the camera do not accurately represent the robot’s environment. The distortions affect the coordinates. A technique to calibrate the camera is required to transform the skewed coordinates of the objects in the image to the physical coordinates, which define their real-world position. In this study, a method is proposed for camera calibration using an artificial neural network (ANN) in a two-step process. First, ANN was used to select the camera height and the lens focal lengths for high accuracy. Second, ANN was used to map a coordinate transformation from the camera coordinates to the physical coordinates. During the learning process, the weight of each node in the ANN model changed until the best architecture is reached. The experiments thus resulted in an optimum ANN architecture of 2×4×25×2. The accuracy and efficiency of the camera calibration method were obtained by relearning using the ANN whenever changes to the environmental occurred. Relearning was done using the new input data set for each respective environmental change. Based on our experiments, the average transformation error of the calibration method, using many types of camera, camera positions, camera heights, lens sizes, and focal lengths, was 0.18283 cm. 
ISSN : 2194-5357 
Journal Name : Advances in Intelligent Systems and Computing 
Volume : 345 
Issue Number : 2015 
Publishing Year : 1436 AH
2015 AD
 
Article Type : Article 
Added Date : Monday, March 7, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Anton Satria PrabuwonoSatria Prabuwono, Anton ResearcherDoctorateantonsatria@eu4m.eu

Files

File NameTypeDescription
 38350.pdf pdf 

Back To Researches Page