State Estimation for Bipedal Robot HyPer-1

This thesis presents the development of a real-time state estimation system for the bipedal research platform HyPer-1 using an Extended Kalman Filter (EKF). The system estimates the robot’s full state—including base pose, velocity, orientation, and foot positions—using only proprioceptive sensors such as the IMU, joint encoders, and torque sensors. Contact detection is integrated to selectively refine the state estimate using leg odometry. Validation is planned via comparison with ground truth motion capture and onboard filters.