@TechReport{Albl-TR-2014-26, IS = { zkontrolovano 20 Jan 2015 }, UPDATE = { 2015-01-20 }, author = {Albl, {\v C}en{\v e}k and Zuzana, K{\'u}kelov{\'a} and Pajdla, Tom{\'a}{\v s}}, title = {{RnP} = Rolling Shutter Absolute Pose Problem}, institution = {Center for Machine Perception, K13133 FEE Czech Technical University}, address = {Prague, Czech Republic}, year = {2014}, month = {December}, type = {Research Report}, number = {CTU--CMP--2014--26}, issn = {1213-2365}, pages = {9}, figures = {10}, authorship = {34-33-33}, project = {SGS13/202/OHK3/3T/13,FP7-SPACE-2012-312377,TA02011275}, annote = {We present a minimal, non-iterative solution to the absolute pose problem for images from rolling shutter cameras. Absolute pose problem is a key problem in computer vision and rolling shutter is present in a vast majority of today's digital cameras. We propose several rolling shutter camera models and verify their feasibility for a polynomial solver. A solution based on linearized camera model is chosen and verified in several experiments. We use a linear approximation to the camera orientation, which is meaningful only around the identity rotation. We show that the standard P3P algorithm is able to estimate camera orientation within 6 degrees for camera rotation velocity as high as 30deg/frame. Therefore we can use the standard P3P algorithm to estimate camera orientation and to bring the camera rotation matrix close to the identity. Using this solution, camera position, orientation, translational velocity and angular velocity can be computed using six 2D-to-3D correspondences, with oreintation error under half a degree and relative position error under 2\%. A significant improvement in terms of the number of inliers in RANSAC is demonstrated.}, keywords = {absolute pose, SfM, rolling shutter}, comment = {Confidential.}, }