  Recent direct visual odometry and SLAM algorithms have demonstrated
impressive levels of precision. However, they require a photometric camera
calibration in order to achieve competitive results. Hence, the respective
algorithm cannot be directly applied to an off-the-shelf-camera or to a video
sequence acquired with an unknown camera. In this work we propose a method for
online photometric calibration which enables to process auto exposure videos
with visual odometry precisions that are on par with those of photometrically
calibrated videos. Our algorithm recovers the exposure times of consecutive
frames, the camera response function, and the attenuation factors of the sensor
irradiance due to vignetting. Gain robust KLT feature tracks are used to obtain
scene point correspondences as input to a nonlinear optimization framework. We
show that our approach can reliably calibrate arbitrary video sequences by
evaluating it on datasets for which full photometric ground truth is available.
We further show that our calibration can improve the performance of a
state-of-the-art direct visual odometry method that works solely on pixel
intensities, calibrating for photometric parameters in an online fashion in
realtime.
