An Iterative Kalman Filter Approach to Camera Calibration

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• 作者：Carlos Ricolfe-Viala ; Antonio-José Sánchez-Salmerón
• 关键词：camera calibration ; Kalman filter ; sampling ; interval computation
• 刊名：Lecture Notes in Computer Science
• 出版年：2008
• 出版时间：2008
• 年：2008
• 卷：5259
• 期：1
• 页码：137-146
• 全文大小：245.5 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

An iterative camera calibration approach is presented in this paper. This approach allows computing the optimal camera parameters for a given set of data. If non linear estimation process is done, a risk of reaching a local minimum exists. With this method this risk is reduced and a best estimation is achieved. By one hand, an iterative improving of the estimated camera parameters is done maximizing a posteriori probability density function (PDF) for a given set of data. To resolve it, a Kalman filter is used based on the Bayesian standpoint. Each update is carried out starting with a new set of data, its covariance matrix and a previous estimation of the parameters. In this case, a different management of the input data is done to extract all its information. By the other hand, apart from the calibration algorithm, a method to compute an interval which contains camera parameters is presented. It is based on computing the covariance matrix of the estimated camera parameters.