  This paper presents a novel method for visual-inertial odometry. The method
is based on an information fusion framework employing low-cost IMU sensors and
the monocular camera in a standard smartphone. We formulate a sequential
inference scheme, where the IMU drives the dynamical model and the camera
frames are used in coupling trailing sequences of augmented poses. The novelty
in the model is in taking into account all the cross-terms in the updates, thus
propagating the inter-connected uncertainties throughout the model. Stronger
coupling between the inertial and visual data sources leads to robustness
against occlusion and feature-poor environments. We demonstrate results on data
collected with an iPhone and provide comparisons against the Tango device and
using the EuRoC data set.
