Research on mobile robot navigation has produced two major paradigms for mapping
indoor environments: grid-based and topological. While grid-based methods produce …

Autonomous robots must be able to learn and maintain models of their environments.
Research on mobile robot navigation has produced two major paradigms for mapping …

This paper presents an algorithms for learning occupancy grid maps with mobile robots
equipped with range finders, such as sonar sensors. Our approach employs the EM …

Topological maps represent the world as a network of nodes and arcs: the nodes are
distinctive places in the environment and the arcs represent paths between places. A …

Autonomous place detection has long been a major hurdle to topological map-building
techniques. Theoretical work on topological mapping has assumed that places can be …

This paper presents an adaptive method that allows mobile robots to learn cognitive maps of
indoor environments incrementally and online. Our approach models the environment. By …

Mobile robot typically has limited on-board resources and may be applied in different indoor
environment. Thus, it is necessary that they can learn a map and navigate themselves …

This chapter surveys basic methods for learning maps and high speed autonomous
navigation for indoor mobile robots. The methods have been developed in our lab over the …

In this chapter we describe popular ways to represent the environment of a mobile robot. For
indoor environments, which are often stored using two-dimensional representations, we …

Mobile robots are dependent upon a model of the environment for many of their basic
functions. Locally accurate maps are critical to collision avoidance, while large-scale maps …