A robust estimation method for camera calibration with known rotation

Abstract

AbstractImagine that hundreds of video streams, taken by mobile phones during a rock concert, are uploaded to a server. One attractive application of such prominent dataset is to allow a user to create his own video with a deliberately chosen but virtual camera trajectory. This is the challenge addressed by the Scenenet project, programme FET‐Open SME (GA 309169). One of the most basic tasks related to creating such a video, is the spatial registration of the individual video stream prior to combining them into a new virtual video. At its core, this requires to estimate the pairwise relative geometry of images taken by different cameras. This is known as the relative pose problem [1], and is fundamental to many computer vision algorithms. In practice, efficiency and robustness is of highest relevance for big data applications as the ones address in the EU‐FET_SME project Scenenet. In this paper, we present an improved algorithm that exploits additional data from inertial sensors, such as accelerometer, magnetometer or gyroscopes, which by now are available in most mobile phones. We demonstrate that in this case the pose estimation problem reduces to a 3‐dimensional least‐squares optimization problem, which can be readily implemented into a 3‐points RANSAC scheme. Experimental results on synthetic and real data demonstrate the accuracy and efficiency of our algorithm. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)