We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an inertial measurement unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.
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In principle, our localization method can be used on terrain with variable slopes. However, using the NAO built-in locomotion functions, relying on the flat floor assumption, it is only possible to allow very small variations in the slope that are not distinguishable from measurement noise. With sufficiently high slope values the robot falls down.
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Oriolo, G., Paolillo, A., Rosa, L. et al. Humanoid odometric localization integrating kinematic, inertial and visual information. Auton Robot 40, 867–879 (2016). https://doi.org/10.1007/s10514-015-9498-0
- Humanoid robots
- Visual SLAM