Humanoid localization and navigation using a visual memory

A visual memory (VM) is a topological map in which a set of key images organized in form of a graph represents an environment. In this paper, a navigation strategy for humanoid robots addressing the problems of localization, visual path planning and path following based on a VM is proposed. Assuming that the VM is given, the main contributions of the paper are: 1) A novel pure vision-based localization method. 2) The introduction of the estimated rotation between key images in the path planning stage to benefit paths with enough visual information and with less effort of robot rotation. 3) The integration of the complete navigation strategy and its experimental evaluation with a Nao robot in an unstructured environment. The humanoid robot is modeled as a holonomic system and the strategy might be used in different scenarios like corridors, uncluttered or cluttered environments.