Vibration-Resilient LiDAR-Inertial Odometry with External Disturbance Compensation for Quadruped Robots
Quoc Hung Hoang, Gon-Woo Kim
AI summary
Problem
Locomotion-induced vibrations on uneven terrain amplify IMU noise and cause LiDAR motion distortion, severely degrading localization and mapping accuracy for quadruped robots.
Approach
The framework uses time delay estimation within an error-state Kalman filter to explicitly model and compensate for external IMU disturbances during preintegration, which then corrects LiDAR scan distortion in a unified tightly-coupled system.
Key results
- 0.1742 m translation and 0.425 deg rotation error on playground terrain
- Accurately estimates periodic 2 Hz vibration disturbances matching the robot's gait cycle
- Produces smoother trajectories and avoids pose degradation during sharp turns compared to LIO-SAM
- Maintains robust real-time mapping performance with 70 ms processing time
Why it matters
Enables reliable autonomous navigation and mapping for legged robots in highly dynamic, vibration-prone environments where conventional sensors fail.
Abstract
This work presents a tightly coupled LiDAR– inertial odometry (LIO) framework tailored for quadruped robots operating under vibration and fluctuating conditions. By integrating time delay estimation (TDE) into an error-state Kalman filter (ESKF), external disturbances affecting the IMU are explicitly estimated and compensated during IMU pre- integration, significantly reducing vibration-induced errors. The resulting refined IMU poses are further used to correct LiDAR motion distortion, enabling a unified refinement process. This leads to smoother trajectories, improved localization accuracy, and enhanced robustness against both environmental and sensor uncertainties. The proposed framework is validated through real-time deployment on a quadruped robot.