Vision-based Odometric Localization for humanoids using a kinematic EKF

Abstract

We propose an odometric system for localizing a walking humanoid robot using standard sensory equipment, i.e., a camera, an Inertial Measurement Unit, joint encoders and foot pressure sensors. Our method has the prediction-correction structure of an Extended Kalman Filter. At each sampling instant, position and orientation of the torso are predicted on the basis of the differential kinematic map from the support foot to the torso, using encoder data from the support joints. The actual measurements coming from the camera (head position and orientation reconstructed by a V-SLAM algorithm) and the Inertial Measurement Unit (torso orientation) are then compared with their predicted values to correct the estimate. The filter is made aware of the current placement of the support foot by an asynchronous update mechanism triggered by the pressure sensors. An experimental validation on the humanoid NAO shows the satisfactory performance of the proposed method.