A neural network-based camera calibration method for mobile robot localization problems

  • Authors:

  • Anmin Zou;Zengguang Hou;Lejie Zhang;Min Tan

  • Affiliations:

  • Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

To navigate reliably in indoor environments, a mobile robot has to know where it is. The methods for pose (position and orientation) estimation can be roughly divided into two classes: methods for keeping track of the robot's pose and methods for global pose estimation [1]. In this paper, a neural network-based camera calibration method is presented for the global localization of mobile robots with monocular vision. In order to localize and navigate the robot using vision information, the camera has to be first calibrated. We calibrate the camera using the neural network based method, which can simplify the tedious calibration process and does not require specialized knowledge of the 3D geometry and computer vision. The monocular vision is used to initialize and recalibrate the robot's pose, and the extended Kalman filter is adopted to keep track of the mobile robot's pose.