  Estimating the 6D pose of objects is beneficial for robotics tasks such as
transportation, autonomous navigation, manipulation as well as in scenarios
beyond robotics like virtual and augmented reality. With respect to single
image pose estimation, pose tracking takes into account the temporal
information across multiple frames to overcome possible detection
inconsistencies and to improve the pose estimation efficiency. In this work, we
introduce a novel Deep Neural Network (DNN) called VIPose, that combines
inertial and camera data to address the object pose tracking problem in
real-time. The key contribution is the design of a novel DNN architecture which
fuses visual and inertial features to predict the objects' relative 6D pose
between consecutive image frames. The overall 6D pose is then estimated by
consecutively combining relative poses. Our approach shows remarkable pose
estimation results for heavily occluded objects that are well known to be very
challenging to handle by existing state-of-the-art solutions. The effectiveness
of the proposed approach is validated on a new dataset called VIYCB with RGB
image, IMU data, and accurate 6D pose annotations created by employing an
automated labeling technique. The approach presents accuracy performances
comparable to state-of-the-art techniques, but with the additional benefit of
being real-time.
