  Photometric calibration is essential to many computer vision applications.
One of its key benefits is enhancing the performance of Visual SLAM, especially
when it depends on a direct method for tracking, such as the standard KLT
algorithm. Another advantage could be in retrieving the sensor irradiance
values from measured intensities, as a pre-processing step for some vision
algorithms, such as shape-from-shading. Current photometric calibration systems
rely on a joint optimization problem and encounter an ambiguity in the
estimates, which can only be resolved using ground truth information. We
propose a novel method that solves for photometric parameters using a
sequential estimation approach. Our proposed method achieves high accuracy in
estimating all parameters; furthermore, the formulations are linear and convex,
which makes the solution fast and suitable for online applications. Experiments
on a Visual Odometry system validate the proposed method and demonstrate its
advantages.
