Using n-grams models for visual semantic place recognition

Semantic mapping (see (Nüchter and Hertzberg, 2008)) is a relatively new field in robotics which aims to give the robot a high-level, human-compatible, understanding of its environment in order to ease the integration of robots in daily environments, notably homes or workplaces. Such environments are usually composed of discrete places which correspond to different functions. For instance a house is usually made of different rooms and corridors used to move between them. Such places are called semantic places because they are defined in high-level human concepts as opposed to traditional low-level landmarks used in robot mapping. In this context, it's important for the robot to be able to recognize in which place or category of places it lies.