Body Velocity Estimation in a Leg�Wheel Transformable Robot without a Priori Knowledge of Leg�Wheel Ground Contacts

The state estimation of legged robots often relies on ground contact detection. However, due to complex mechanisms and other factors, ground contact detection can be challenging to obtain in certain situations. This paper presents a velocity estimation method that combines inertia measurement unit (IMU) and encoders, allowing estimation without using the ground contact state as the a priori. In this paper, the initial estimate derived from IMU integration is refined. Following the computation of velocity and ground contact state probabilities using encoder data, these probabilities are employed to modify particle weights within the particle filter framework. Subsequent resampling ensures that the contact status converges toward the correct result. This paper tests the algorithm through simulations and validates the method with physical experiments, showcasing the feasibility of concurrent ground contact state and velocity estimation.