  Humanoid robots without internal sensors such as a compass tend to lose their
orientation after a fall. Furthermore, re-initialisation is often ambiguous due
to symmetric man-made environments. The room-awareness module proposed here is
inspired by the results of psychological experiments and improves existing
self-localization strategies by mapping and matching the visual background with
colour histograms. The matching algorithm uses a particle-filter to generate
hypotheses of the viewing directions independent of the self-localization
algorithm and generates confidence values for various possible poses. The
robot's behaviour controller uses those confidence values to control
self-localization algorithm to converge to the most likely pose and prevents
the algorithm from getting stuck in local minima. Experiments with a symmetric
Standard Platform League RoboCup playing field with a simulated and a real
humanoid NAO robot show the significant improvement of the system.
