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 …

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

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 …

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 …

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 …

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 …

Robotic mapping is the process of automatically constructing an environment representation
using mobile robots. We address the problem of semantic mapping, which consists of using …

In this paper we address the problem of autonomous navigation in very large unstructured
environments. A new hybrid metric map (HYMM) structure is presented that combines …

This paper addresses the problem of building large-scale geometric maps of indoor
environments with mobile robots. It poses the map building problem as a constrained …

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 …