LLIO: LiDAR-Kinematic-Inertial Odometry With Ground Contact Constraints for Legged Robots

Abstract

This letter presents a robust multi-sensor fusion framework for state estimation in legged robots (LLIO) based on an iterated extended Kalman filter. To address the limitations of IMU priori estimation, which often leads to legged robot localization errors or failures, our method integrates the contact constraints of the robot's leg kinematics with the ground. By introducing a sliding window-based ground contact constraint module, we effectively combine the contact state of the legged robot's foot with ground features, enhancing the constraints in complex environments and reduce localization drift. Additionally, factor graph optimization minimizes global cumulative drift. The proposed method has been extensively evaluated through numerous experiments and relevant public datasets. The results demonstrate that our approach significantly reduces local drift and better computational efficiency.