Probabilistic Cooperative Localization and Mapping in Practice

Abstract 

In this paper we present a probabilistic framework for the
reduction in the uncertainty of a moving robot pose during
exploration by using a second robot to assist. A Monte Carlo
Simulation technique (specifically, a Particle Filter) is employed
in order to model and reduce the accumulated odometric
error. Furthermore, we study the requirements to obtain
an accurate yet timely pose estimate. A team of two
robots is employed to explore an indoor environment in this
paper, although several aspects of the approach have been
extended to larger groups. The concept behind our exploration
strategy has been presented previously and is based on
having one robot carry a sensor that acts as a .robot tracker.
to estimate the position of the other robot. By suitable use
of the tracker as an appropriate motion-control mechanism
we can sweep areas of free space between the stationary and
the moving robot and generate an accurate graph-based description
of the environment. This graph is used to guide
the exploration process. Complete exploration without any
overlaps is guaranteed as a result of the guidance provided
by the dual graph of the spatial decomposition (triangulation)
of the environment. We present experimental results
from indoor experiments in our laboratory and from more
complex simulated experiments.