  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.
