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Robust and accurate six degree-of-freedom tracking on portable devicesremains a challenging problem, especially on small hand-held devices such assmartphones. For improved robustness and accuracy, complementary movementinformation from an IMU and a camera is often fused. Conventionalvisual-inertial methods fuse information from IMUs with a sparse cloud offeature points tracked by the device camera. We consider a visually denseapproach, where the IMU data is fused with the dense optical flow fieldestimated from the camera data. Learning-based methods applied to the fullimage frames can leverage visual cues and global consistency of the flow fieldto improve the flow estimates. We show how a learning-based optical flow modelcan be combined with conventional inertial navigation, and how ideas fromprobabilistic deep learning can aid the robustness of the measurement updates.The practical applicability is demonstrated on real-world data acquired by aniPad in a challenging low-texture environment.

Movement Tracking by Optical Flow Assisted Inertial Navigation