Robust visual odometry to irregular illumination changes with RGB-D camera

Sensitivity to illumination conditions poses a challenge when utilizing visual odometry (VO) in various applications. To make VO robust with respect to illumination conditions, they need to be considered explicitly. In this paper, we propose a direct visual odometry method which can handle illumination changes by considering an affine illumination model to compensate abrupt, local light variations during direct motion estimation process. The core of our proposed method is to estimate the relative camera pose and the parameters of the illumination changes by minimizing the sum of squared photometric error with efficient second-order minimization. We evaluate the performance of the proposed algorithm on synthetic and real RGB-D datasets with ground-truth. Our result implies that the proposed method successfully estimates 6-DoF pose under significant illumination changes whereas existing direct visual odometry methods either fail or lose accuracy.