```@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},
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.},
}

```