An Efficient Camera Calibration Algorithm with Bundle Adjustment Technique (BAT)

Camera calibration has been studied extensively in computer vision and photogrammetry. A camera calibration scheme that uses a simple reference object is superior in real environments, as it yields systems that can respond quickly to various camera configurations in indoors and outdoors. In this work a new calibration technique using 1D objects (points aligned on a line), and filling the missing dimension in calibration is proposed. A closed-form solution is developed if five observations of such a 1D object are made a simple and practical calibration technique is proposed that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each has three collinear points, one of which is shared. The basic equations needed to realize camera calibration are derived from just five points observed on a single image that captures the objects. A new camera calibration algorithm that estimates the camera parameters based on the basic equations (Closed-form solution) and optimizes them by the Bundle Adjustment Technique (BAT).