  Estimating the correspondences between pixels in sequences of images is a
critical first step for a myriad of tasks including vision-aided navigation
(e.g., visual odometry (VO), visual-inertial odometry (VIO), and visual
simultaneous localization and mapping (VSLAM)) and anomaly detection. We
introduce a new unsupervised deep neural network architecture called the Visual
Inertial Flow (VIFlow) network and demonstrate image correspondence and optical
flow estimation by an unsupervised multi-hypothesis deep neural network
receiving grayscale imagery and extra-visual inertial measurements. VIFlow
learns to combine heterogeneous sensor streams and sample from an unknown,
un-parametrized noise distribution to generate several (4 or 8 in this work)
probable hypotheses on the pixel-level correspondence mappings between a source
image and a target image . We quantitatively benchmark VIFlow against several
leading vision-only dense correspondence and flow methods and show a
substantial decrease in runtime and increase in efficiency compared to all
methods with similar performance to state-of-the-art (SOA) dense correspondence
matching approaches. We also present qualitative results showing how VIFlow can
be used for detecting anomalous independent motion.
