  Traditional monocular direct visual odometry (DVO) is one of the most famous
methods to estimate the ego-motion of robots and map environments from images
simultaneously. However, DVO heavily relies on high-quality images and accurate
initial pose estimation during tracking. With the outstanding performance of
deep learning, previous works have shown that deep neural networks can
effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular
image sequences in the unsupervised manner. However, these unsupervised deep
learning-based frameworks cannot accurately generate the full trajectory of a
long monocular video because of the scale-inconsistency between each pose. To
address this problem, we use several geometric constraints to improve the
scale-consistency of the pose network, including improving the previous loss
function and proposing a novel scale-to-trajectory constraint for unsupervised
training. We call the pose network trained by the proposed novel constraint as
TrajNet. In addition, a new DVO architecture, called deep direct sparse
odometry (DDSO), is proposed to overcome the drawbacks of the previous direct
sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on
the KITTI dataset show that the proposed constraints can effectively improve
the scale-consistency of TrajNet when compared with previous unsupervised
monocular methods, and integration with TrajNet makes the initialization and
tracking of DSO more robust and accurate.
