  We present an unsupervised deep neural network approach to the fusion of
RGB-D imagery with inertial measurements for absolute trajectory estimation.
Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns
to perform visual-inertial odometry (VIO) without inertial measurement unit
(IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias
or white noise) or the extrinsic calibration between an IMU and camera. The
network learns to integrate IMU measurements and generate hypothesis
trajectories which are then corrected online according to the Jacobians of
scaled image projection errors with respect to a spatial grid of pixel
coordinates. We evaluate our network against state-of-the-art (SOA)
visual-inertial odometry, visual odometry, and visual simultaneous localization
and mapping (VSLAM) approaches on the KITTI Odometry dataset and demonstrate
competitive odometry performance.
