  With the success of deep learning based approaches in tackling challenging
problems in computer vision, a wide range of deep architectures have recently
been proposed for the task of visual odometry (VO) estimation. Most of these
proposed solutions rely on supervision, which requires the acquisition of
precise ground-truth camera pose information, collected using expensive motion
capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose
an unsupervised paradigm for deep visual odometry learning. We show that using
a noisy teacher, which could be a standard VO pipeline, and by designing a loss
term that enforces geometric consistency of the trajectory, we can train
accurate deep models for VO that do not require ground-truth labels. We
leverage geometry as a self-supervisory signal and propose "Composite
Transformation Constraints (CTCs)", that automatically generate supervisory
signals for training and enforce geometric consistency in the VO estimate. We
also present a method of characterizing the uncertainty in VO estimates thus
obtained. To evaluate our VO pipeline, we present exhaustive ablation studies
that demonstrate the efficacy of end-to-end, self-supervised methodologies to
train deep models for monocular VO. We show that leveraging concepts from
geometry and incorporating them into the training of a recurrent neural network
results in performance competitive to supervised deep VO methods.
