Center of Mass (CoM) estimation realizes a crucial role in legged locomotion. Most walking pattern generators and real-time gait stabilizers commonly assume that the CoM position and velocity are available for feedback. In this thesis we present one of the first 3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain and the body’s angular momentum rate and thus effectively couples the frontal with the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing. Nevertheless, it is common practice to transform the measurements to a world frame of reference and estimate the CoM with respect to the world frame. Consequently, the robot’s base and support foot pose are mandatory and need to be co-estimated. To this end, we extend a well-established in literature floating mass estimator to account for the support foot dynamics and fuse kinematic-inertial measurements with the Error State Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In such a way, a cascade state estimation scheme consisting of a base and a CoM estimator is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic drift caused by slippage during walking. Unfortunately, such measurements suffer from outliers in a dynamic environment, since frequently it is assumed that only the robot is in motion and the world around is static. Thus, we introduce the Robust Gaussian ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW is robustified and is suitable for dynamic human environments. In ...


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