Humanoid navigation using a visual memory with obstacle avoidance

We present a complete humanoid navigation scheme based on a topological map known as visual memory (VM), which is composed by a set of key images acquired offline by means of a supervised teaching phase (human-guided). Our autonomous navigation scheme integrates the humanoid localization in the VM, a visual path planner and a path follower with obstacle avoidance. We propose a pure vision-based localization algorithm that takes advantage of the topological structure of the VM to find the key image that best fits the current image in terms of common visual information. In addition, the visual path planner benefits obstacle-free paths. The VM is updated when a new obstacle is detected with an RGB-D camera mounted on the humanoid's head. The visual path following and obstacle avoidance problems are formulated in a unified sensor-based framework in which, a hierarchy of tasks is defined, and the transitions of consecutive and hierarchical tasks are performed smoothly to avoid instability of the humanoid. An extensive experimental evaluation using the NAO platform shows the good performance of the navigation scheme.