  Self-supervised VO methods have shown great success in jointly estimating
camera pose and depth from videos. However, like most data-driven methods,
existing VO networks suffer from a notable decrease in performance when
confronted with scenes different from the training data, which makes them
unsuitable for practical applications. In this paper, we propose an online
meta-learning algorithm to enable VO networks to continuously adapt to new
environments in a self-supervised manner. The proposed method utilizes
convolutional long short-term memory (convLSTM) to aggregate rich
spatial-temporal information in the past. The network is able to memorize and
learn from its past experience for better estimation and fast adaptation to the
current frame. When running VO in the open world, in order to deal with the
changing environment, we propose an online feature alignment method by aligning
feature distributions at different time. Our VO network is able to seamlessly
adapt to different environments. Extensive experiments on unseen outdoor
scenes, virtual to real world and outdoor to indoor environments demonstrate
that our method consistently outperforms state-of-the-art self-supervised VO
baselines considerably.
