# Robust Legged Robot State Estimation Using Factor Graph Optimization

## TL;DR

This paper introduces a factor graph optimization approach for quadruped robot state estimation that fuses inertial, leg, and visual data, significantly reducing position errors in challenging outdoor environments.

## Contribution

It presents a novel factor graph method that improves robustness and accuracy of state estimation for legged robots in complex terrains and dynamic motions.

## Key findings

- Reduced relative position error by up to 55%
- Decreased absolute position error by 76%
- Effective in outdoor industrial scenarios with various terrains

## Abstract

Legged robots, specifically quadrupeds, are becoming increasingly attractive for industrial applications such as inspection. However, to leave the laboratory and to become useful to an end user requires reliability in harsh conditions. From the perspective of state estimation, it is essential to be able to accurately estimate the robot's state despite challenges such as uneven or slippery terrain, textureless and reflective scenes, as well as dynamic camera occlusions. We are motivated to reduce the dependency on foot contact classifications, which fail when slipping, and to reduce position drift during dynamic motions such as trotting. To this end, we present a factor graph optimization method for state estimation which tightly fuses and smooths inertial navigation, leg odometry and visual odometry. The effectiveness of the approach is demonstrated using the ANYmal quadruped robot navigating in a realistic outdoor industrial environment. This experiment included trotting, walking, crossing obstacles and ascending a staircase. The proposed approach decreased the relative position error by up to 55% and absolute position error by 76% compared to kinematic-inertial odometry.