Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information

TL;DR

This paper introduces a robust LiDAR-IMU-leg odometry system for quadruped robots that uses an online learned neural leg kinematics model with tactile feedback, improving accuracy in challenging terrains.

Contribution

It presents a novel online learning-based leg kinematics model that incorporates tactile information and integrates it into a unified odometry estimation framework.

Findings

Outperforms state-of-the-art odometry methods in challenging terrains.

Successfully adapts to terrain and load changes through online training.

Demonstrates robustness in featureless and deformable environments.

Abstract

In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed an online learning-based leg kinematics model named the neural leg kinematics model, which incorporates tactile information (foot reaction force) to implicitly express the nonlinear dynamics between robot feet and the ground. Online training of this model enhances its adaptability to weight load changes of a robot (e.g., assuming delivery or transportation tasks) and terrain conditions. According to the \textit{neural adaptive leg odometry factor} and online uncertainty estimation of the leg kinematics model-based motion predictions, we jointly solve online training of this kinematics model and odometry estimation on a unified factor graph to retain the consistency of both. The proposed method was verified through real experiments using a quadruped robot in two challenging situations: 1) a sandy beach, representing an extremely featureless area with a deformable terrain, and 2) a campus, including multiple featureless areas and terrain types of asphalt, gravel (deformable terrain), and grass. Experimental results showed that our odometry estimation incorporating the \textit{neural leg kinematics model} outperforms state-of-the-art works. Our project page is available for further details: https://takuokawara.github.io/RAL2025_project_page/