IS = { zkontrolovano 10 Mar 2015 },
  UPDATE  = { 2015-03-10 },
author={Heller, Jan and Pajdla, Tomas},
booktitle={3D Vision (3DV), 2014 2nd International Conference on},
title={World-Base Calibration by Global Polynomial Optimization},
editor={O'Conner, Lisa},
address={Piscataway, USA},
venue={Tokyo, Japan},
annote={This paper presents a novel solution to the world-base
                  calibration problem. It is applicable in situations
                  where a known calibration target is observed by a
                  camera attached to the end effector of a robotic
                  manipulator. The presented method works by
                  minimizing geometrically meaningful error function
                  based on image projections. Our formulation leads to
                  a non-convex multivariate polynomial optimization
                  problem of a constant size. However, we show how
                  such a problem can be relaxed using linear matrix
                  inequality (LMI) relaxations and effectively solved
                  using Semi definite Programming. Although the
                  technique of LMI relaxations guaranties only a lower
                  bound on the global minimum of the original problem,
                  it can provide a certificate of optimality in cases
                  when the global minimum is reached. Indeed, we
                  reached the global minimum for all calibration tasks
                  in our experiments with both synthetic and real
                  data. The experiments also show that the presented
                  method is fast and noise resistant.},
keywords={Calibration;Cameras;Polynomials;Robot kinematics;Robot
                  vision systems;global polynomial
                  optimization;world-base calibration},
project={FP7-ICT-288553 CloPeMa, TA02011275 ATOM},