Learning Spatial Models for Navigation

摘要

Typically, autonomous robot navigation relies on a detailed, accurate map. The associated representations, however, do not readily support human-friendly interaction. The approach reported here offers an alternative: navigation with a spatial model and commonsense qualitative spatial reasoning. Both are based on research about how people experience and represent space. The spatial model quickly develops as the result of incremental learning while the robot moves through its environment. In extensive empirical testing, qualitative spatial reasoning principles that reference this model support increasingly effective navigation in a variety of built spaces.