Learning Inertial Odometry for Dynamic Legged Robot State Estimation

TL;DR

This paper presents a learned inertial displacement measurement method that, when fused with leg odometry, significantly reduces drift and improves state estimation accuracy for legged robots in challenging, vision-degraded environments.

Contribution

It introduces a novel neural network-based inertial displacement estimator that enhances proprioceptive state estimation in difficult terrains and environments.

Findings

37% reduction in relative pose error in challenging terrains

22% error reduction in visually degraded environments

Effective fusion with traditional leg odometry improves robustness

Abstract

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.