HR²-KILO: A High-Rate, Robust, Kinematic-Inertial-LiDAR Odometry for Humanoid Robots

In this letter, we present a high-rate and robust multi-sensor fusion framework for state estimation of humanoid robots, named HR²-KILO. To handle the inherently high-dynamic characteristics of humanoid robots, the proposed framework tightly couples the measurements from the joint encoder, inertial sensor, and LiDAR. We estimate states within the error-state Kalman filter, incorporating the pointwise update strategy, IMU measurement model, and multiple leg kinematic information. Moreover, acceleration fluctuations, foot positions, and the history map are utilized for online contact detection without any contact sensors. The overall system fully utilizes the available multi-source information, making it compact and easy to deploy. Extensive experiments are conducted both on the public dataset and in the real world, including different humanoid robots and diverse scenarios. The results demonstrate that HR²-KILO achieves extremely high rate output and lower drift compared to state-of-the-art LiDAR-inertial(-kinematic) methods. To contribute to the community, the source code and the multi-sensor humanoid dataset are released.