Inertial sensors play a pivotal role in indoor localization, which in turn
lays the foundation for pervasive personal applications. However, low-cost
inertial sensors, as commonly found in smartphones, are plagued by bias and
noise, which leads to unbounded growth in error when accelerations are double
integrated to obtain displacement. Small errors in state estimation propagate
to make odometry virtually unusable in a matter of seconds. We propose to break
the cycle of continuous integration, and instead segment inertial data into
independent windows. The challenge becomes estimating the latent states of each
window, such as velocity and orientation, as these are not directly observable
from sensor data. We demonstrate how to formulate this as an optimization
problem, and show how deep recurrent neural networks can yield highly accurate
trajectories, outperforming state-of-the-art shallow techniques, on a wide
range of tests and attachments. In particular, we demonstrate that IONet can
generalize to estimate odometry for non-periodic motion, such as a shopping
trolley or baby-stroller, an extremely challenging task for existing
techniques.Comment: To appear in AAAI18 (Oral