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

Qingrui Zhao, Mingyuan Li, Yongliang Shi, Xuechao Chen, Zhangguo Yu, Lianqiang Han, Zhenyuan Fu, Jintao Zhang, Chao Li, Yuanxi Zhang, Qiang Huang

TL;DR

This work tackles the challenge of accurate, high-frequency state estimation for biped robots under intermittent ground contact. It proposes LIKO, a tightly coupled LiDAR–Inertial–Kinematic Odometry framework that uses an iterated extended Kalman filter and online foothold estimation to deliver ~1 kHz updates. The approach achieves state-of-the-art accuracy across multiple datasets and demonstrates robust velocity and foothold estimation, with a public dataset and open-source code. The findings suggest substantial practical benefits for real-time biped control and locomotion in uneven environments.

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.

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

TL;DR

This work tackles the challenge of accurate, high-frequency state estimation for biped robots under intermittent ground contact. It proposes LIKO, a tightly coupled LiDAR–Inertial–Kinematic Odometry framework that uses an iterated extended Kalman filter and online foothold estimation to deliver ~1 kHz updates. The approach achieves state-of-the-art accuracy across multiple datasets and demonstrates robust velocity and foothold estimation, with a public dataset and open-source code. The findings suggest substantial practical benefits for real-time biped control and locomotion in uneven environments.

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.
Paper Structure (22 sections, 27 equations, 5 figures, 2 tables)

This paper contains 22 sections, 27 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Biped Robot State Estimation
  4. LiDAR-Inertial Odometry
  5. Methodology
  6. System Overview
  7. State Representation
  8. Propagation Model
  9. LiDAR Measurement
  10. Kinematic Measurement
  11. Contact Classification
  12. Velocity Measurement
  13. Position Measurement
  14. State Update
  15. Experiments
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Left: The proposed LIKO state estimation system is tested on a BHR-B3 biped robot. The robot's body coordinate is aligned with the IMU coordinate. Right: Linear velocity estimation comparison between proposed LIKO (green), Inertial Kinematic odometry Contact-aided InEKF hartley2020contact (blue), and LiDAR-Inertial odometry (LIO) FastLIO2 xu2022fast (orange) against ground truth (gray). The velocity estimated by FastLIO2 is plotted in scatter form since its update frequency is at 10Hz with Velodyne LiDAR, while other methods operate at 1kHz. The red boxes highlighted where the proposed method exhibits more accurate velocity estimation than others.
  • Figure 2: System overview of LIKO. The system receives input from a LiDAR, an IMU, joint encoders, and F/T sensors. The IMU is used for state propagation, while LiDAR, joint encoder, and F/T sensors generate three different types of state measurement. The formulation of the iterated Kalman filter is discussed in Section \ref{['sec: method']}.
  • Figure 3: Top: XY position trajectory estimation results of three LIKO variants used in the ablation study against ground truth (gray). Bottom: The Z Position trajectory estimation results of three LIKO variants used in the ablation study against ground truth (gray).
  • Figure 4: Left: Linear velocity comparison between LIKO (green) and Contact-aided InEKF hartley2020contact (blue) against ground truth (orange). Right: Zoomed-in plot corresponding to the gray area of the left one. The more accurate and smoother velocity tracking allows for a better closed-loop control performance.
  • Figure 5: Left: BHR-B3 walking in the MoCap room. Head and feet are equipped with MoCap markers. Right: Top-down view of the trajectory and contact position estimation (green) and the ground truth (gray). In the legend, "gt." stands for "ground truth" and "est." stands for "estimated".