LIKO: LiDAR, Inertial, and Kinematic Odometry for Bipedal Robots

TL;DR

This paper introduces LIKO, a high-frequency, accurate state estimation method for biped robots using LiDAR, inertial, and kinematic data within an iterated extended Kalman filter framework, improving control and estimation accuracy.

Contribution

The paper presents a novel tightly-coupled LiDAR-Inertial-Kinematic Odometry approach with foot contact modeling, achieving higher output frequency and better accuracy for biped robot state estimation.

Findings

Achieved the best quantitative results among LIO-based methods.

Increased output state frequency to about 1kHz.

Provided a new dataset for biped robot state estimation evaluation.

Abstract

High-frequency and accurate state estimation is crucial for biped robots. This paper presents a tightly-coupled LiDAR-Inertial-Kinematic Odometry (LIKO) for biped robot state estimation based on an iterated extended Kalman filter. Beyond state estimation, the foot contact position is also modeled and estimated. This allows for both position and velocity updates from kinematic measurement. Additionally, the use of kinematic measurement results in an increased output state frequency of about 1kHz. This ensures temporal continuity of the estimated state and makes it practical for control purposes of biped robots. We also announce a biped robot dataset consisting of LiDAR, inertial measurement unit (IMU), joint encoders, force/torque (F/T) sensors, and motion capture ground truth to evaluate the proposed method. The dataset is collected during robot locomotion, and our approach reached the best quantitative result among other LIO-based methods and biped robot state estimation algorithms. The dataset and source code will be available at https://github.com/Mr-Zqr/LIKO.