Multi-IMU Sensor Fusion for Legged Robots

TL;DR

This paper introduces a multi-IMU sensor fusion approach for legged robots that combines inertial, joint, and visual data to achieve accurate, low-drift state estimation during complex locomotion tasks.

Contribution

It proposes a novel sensor fusion framework using multiple IMUs, joint encoders, and cameras integrated through an extended Kalman filter and factor graph for improved state estimation.

Findings

Achieves minimal position deviation in challenging scenarios

Robustly handles ground impact and foot slippage

Provides a comprehensive dataset and implementation

Abstract

This paper presents a state-estimation solution for legged robots that uses a set of low-cost, compact, and lightweight sensors to achieve low-drift pose and velocity estimation under challenging locomotion conditions. The key idea is to leverage multiple inertial measurement units on different links of the robot to correct a major error source in standard proprioceptive odometry. We fuse the inertial sensor information and joint encoder measurements in an extended Kalman filter, then combine the velocity estimate from this filter with camera data in a factor-graph-based sliding-window estimator to form a visual-inertial-leg odometry method. We validate our state estimator through comprehensive theoretical analysis and hardware experiments performed using real-world robot data collected during a variety of challenging locomotion tasks. Our algorithm consistently achieves minimal position deviation, even in scenarios involving substantial ground impact, foot slippage, and sudden body rotations. A C++ implementation, along with a large-scale dataset, is available at https://github.com/ShuoYangRobotics/Cerberus2.0.