This article deals with specific aspects concerning the visual perception process of a humanoid walking machine. An active vision system provides the information about the environment necessary for autonomous goal-oriented locomotion. Due to errors in each stage of the perception process, ideal environment reconstruction is not possible. By modeling these errors, stochastic components can be compensated using a hybrid extended Kalman filter approach with an alternating reference frame, thus reflecting the discontinuous character of biped walking.. The perception results improved by filtering can be used for the autonomous locomotion of the robot. Experiments with the walking machine BARt-UH demonstrate the validity of our approach.

Goal-oriented vision-guided biped locomotion requires a high degree of coordination between perception and walking. How to establish this coordination remains a fundamental and rarely studied problem in legged robotics. Some of our investigations into this field are outlined in this article by presenting recent results in vision-guided biped locomotion. The guidance and control approaches developed are experimentally validated on the biped robot BARt-UH. It is shown how perception techniques are employed in closed-loop for step sequence adaptation and locomotion control of a walking machine.