Vibration-Resilient LiDAR-Inertial Odometry with External Disturbance Compensation for Quadruped Robots

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.