  Unsupervised monocular depth and ego-motion estimation has drawn extensive
research attention in recent years. Although current methods have reached a
high up-to-scale accuracy, they usually fail to learn the true scale metric due
to the inherent scale ambiguity from training with monocular sequences. In this
work, we tackle this problem and propose DynaDepth, a novel scale-aware
framework that integrates information from vision and IMU motion dynamics.
Specifically, we first propose an IMU photometric loss and a cross-sensor
photometric consistency loss to provide dense supervision and absolute scales.
To fully exploit the complementary information from both sensors, we further
drive a differentiable camera-centric extended Kalman filter (EKF) to update
the IMU preintegrated motions when observing visual measurements. In addition,
the EKF formulation enables learning an ego-motion uncertainty measure, which
is non-trivial for unsupervised methods. By leveraging IMU during training,
DynaDepth not only learns an absolute scale, but also provides a better
generalization ability and robustness against vision degradation such as
illumination change and moving objects. We validate the effectiveness of
DynaDepth by conducting extensive experiments and simulations on the KITTI and
Make3D datasets.
