  Robust and accurate six degree-of-freedom tracking on portable devices
remains a challenging problem, especially on small hand-held devices such as
smartphones. For improved robustness and accuracy, complementary movement
information from an IMU and a camera is often fused. Conventional
visual-inertial methods fuse information from IMUs with a sparse cloud of
feature points tracked by the device camera. We consider a visually dense
approach, where the IMU data is fused with the dense optical flow field
estimated from the camera data. Learning-based methods applied to the full
image frames can leverage visual cues and global consistency of the flow field
to improve the flow estimates. We show how a learning-based optical flow model
can be combined with conventional inertial navigation, and how ideas from
probabilistic deep learning can aid the robustness of the measurement updates.
The practical applicability is demonstrated on real-world data acquired by an
iPad in a challenging low-texture environment.
