KLILO: Kalman Filter based LiDAR-Inertial-Leg Odometry for Legged Robots

This paper presents a Kalman filter based LiDAR-Inertial-Leg Odometry (KLILO) system for legged robots to navigate in challenging environments. In particular, we employ the iterated error-state extended Kalman filter framework on manifolds to fuse measurements from the inertial measurement unit (IMU), LiDAR, joint encoders, and contact force sensors in a tightly coupled manner. To assess the performance of KLILO, we build a dataset that encompasses intricate environments with challenging conditions such as dynamic objects and deformable terrains. The results demonstrate that our algorithm can provide efficient and reliable localization in all tests. It exhibits an average improvement of around 40% in positioning accuracy compared to the baselines. Furthermore, we validate KLILO in a challenging navigation task on a real robot, where the LiDAR encounters ineffective measurements.