  Existing unsupervised visual odometry (VO) methods either match pairwise
images or integrate the temporal information using recurrent neural networks
over a long sequence of images. They are either not accurate, time-consuming in
training or error accumulative. In this paper, we propose a method consisting
of two camera pose estimators that deal with the information from pairwise
images and a short sequence of images respectively. For image sequences, a
Transformer-like structure is adopted to build a geometry model over a local
temporal window, referred to as Transformer-based Auxiliary Pose Estimator
(TAPE). Meanwhile, a Flow-to-Flow Pose Estimator (F2FPE) is proposed to exploit
the relationship between pairwise images. The two estimators are constrained
through a simple yet effective consistency loss in training. Empirical
evaluation has shown that the proposed method outperforms the state-of-the-art
unsupervised learning-based methods by a large margin and performs comparably
to supervised and traditional ones on the KITTI and Malaga dataset.
