  We present unsupervised parameter learning in a Gaussian variational
inference setting that combines classic trajectory estimation for mobile robots
with deep learning for rich sensor data, all under a single learning objective.
The framework is an extension of an existing system identification method that
optimizes for the observed data likelihood, which we improve with modern
advances in batch trajectory estimation and deep learning. Though the framework
is general to any form of parameter learning and sensor modality, we
demonstrate application to feature and uncertainty learning with a deep network
for 3D lidar odometry. Our framework learns from only the on-board lidar data,
and does not require any form of groundtruth supervision. We demonstrate that
our lidar odometry performs better than existing methods that learn the full
estimator with a deep network, and comparable to state-of-the-art ICP-based
methods on the KITTI odometry dataset. We additionally show results on lidar
data from the Oxford RobotCar dataset.
