An hierarchical approach to grid-based and topological maps integration for autonomous indoor navigation

Research in mobile robot navigation has been based in approaches that integrate the metric and topological paradigms for mapping indoor environments. While metric methods produce accurate environment representations, their huge data volume and time complexity often prohibits efficient planning in large-scale indoor environments. On the other hand, topological maps can be used in a more efficient way, but accurate and coherent topological maps are often difficult to learn and maintain in large-scale environments. The paper describes an approach that integrates both paradigms: metric and topological. The metric map is learnt using a very simple scheme based on the works of Moravec and Elfes (1988). Then, a topological map is generated on top of the metric map by using a hierarchical structure. The main advantage of the proposed structure is that the partitioning of the metric map into coherent regions is achieved in an unsupervised manner with a low computational time. The paper gives results for autonomous exploration, mapping and planning of a Nomad200 mobile robot in large indoor environments