  This paper introduces a novel proprioceptive state estimator for legged
robots based on a learned displacement measurement from IMU data. Recent
research in pedestrian tracking has shown that motion can be inferred from
inertial data using convolutional neural networks. A learned inertial
displacement measurement can improve state estimation in challenging scenarios
where leg odometry is unreliable, such as slipping and compressible terrains.
Our work learns to estimate a displacement measurement from IMU data which is
then fused with traditional leg odometry. Our approach greatly reduces the
drift of proprioceptive state estimation, which is critical for legged robots
deployed in vision and lidar denied environments such as foggy sewers or dusty
mines. We compared results from an EKF and an incremental fixed-lag factor
graph estimator using data from several real robot experiments crossing
challenging terrains. Our results show a reduction of relative pose error by
37% in challenging scenarios when compared to a traditional kinematic-inertial
estimator without learned measurement. We also demonstrate a 22% reduction in
error when used with vision systems in visually degraded environments such as
an underground mine.
