External Disturbances Compensation for LiDAR-Inertial Odometry Under Vibration Conditions on Quadruped Robot

Abstract

This paper presents a new tightly coupled LiDAR-inertial odometry (LIO) scheme for a quadruped robot operating under fluctuating conditions. By proposing external disturbance modeling, the profile of unknown disturbances and noise on the IMU is effectively characterized using the time delay estimation (TDE) technique. Simultaneously, the IMU orientation and the TDE-based uncertainty model are jointly updated through an Error-State Kalman Filter (ESKF) using a measurement model derived from LiDAR odometry (LO). Thereafter, the output of the ESKF is employed to mitigate vibration effects in the IMU preintegration factor, thereby enhancing the precision and stability of inertial motion estimation. Furthermore, the refined IMU preintegration pose is leveraged to correct LiDAR distortion and improve the accuracy of LO. As a result, the proposed approach achieves optimal performance, smooth trajectories, and enhanced robustness against uncertainties. Finally, the effectiveness of the proposed LIO is evaluated through real-time experiments on a quadruped robot across different scenarios.