  Despite learning-based visual odometry (VO) has shown impressive results in
recent years, the pretrained networks may easily collapse in unseen
environments. The large domain gap between training and testing data makes them
difficult to generalize to new scenes. In this paper, we propose an online
adaptation framework for deep VO with the assistance of scene-agnostic
geometric computations and Bayesian inference. In contrast to learning-based
pose estimation, our method solves pose from optical flow and depth while the
single-view depth estimation is continuously improved with new observations by
online learned uncertainties. Meanwhile, an online learned photometric
uncertainty is used for further depth and pose optimization by a differentiable
Gauss-Newton layer. Our method enables fast adaptation of deep VO networks to
unseen environments in a self-supervised manner. Extensive experiments
including Cityscapes to KITTI and outdoor KITTI to indoor TUM demonstrate that
our method achieves state-of-the-art generalization ability among
self-supervised VO methods.
