Contact-Anchored Proprioceptive Odometry for Quadruped Robots

Reliable odometry for legged robots without cameras or LiDAR remains challenging due to IMU drift and noisy joint velocity sensing. This paper presents a purely proprioceptive state estimator that uses only IMU and motor measurements to jointly estimate body pose and velocity, with a unified formulation applicable to biped, quadruped, and wheel-legged robots. The key idea is to treat each contacting leg as a kinematic anchor: joint-torque–based foot wrench estimation selects reliable contacts, and the corresponding footfall positions provide intermittent world-frame constraints that suppress long-term drift. To prevent elevation drift during extended traversal, we introduce a lightweight height clustering and time-decay correction that snaps newly recorded footfall heights to previously observed support planes. To improve foot velocity observations under encoder quantization, we apply an inverse-kinematics cubature Kalman filter that directly filters foot-end velocities from joint angles and velocities. The implementation further mitigates yaw drift through multi-contact geometric consistency and degrades gracefully to a kinematics-derived heading reference when IMU yaw constraints are unavailable or unreliable. We evaluate the method on four quadruped platforms (three Astrall robots and a Unitree Go2 EDU) using closed-loop trajectories. On Astrall point-foot robotA, a $\sim$200,m horizontal loop and a $\sim$15,m vertical loop return with 0.1638,m and 0.219,m error, respectively; on wheel-legged robotB, the corresponding errors are 0.2264,m and 0.199,m. On wheel-legged robot~C, a $\sim$700,m horizontal loop yields 7.68,m error and a $\sim$20,m vertical loop yields 0.540,m error. Unitree Go2 EDU closes a $\sim$120,m horizontal loop with 2.2138,m error and a $\sim$8,m vertical loop with less than 0.1,m vertical error. github.com/ShineMinxing/Ros2Go2Estimator.git