|This paper describes an on-line
algorithm for multi-robot simultaneous localization and mapping (SLAM).
The starting point is the single-robot Rao-Blackwellized particle filter
described by Hähnel et al., and three key generalizations are made.
First, the particle filter is extended to handle multi-robot SLAM problems
in which the initial pose of the robots is known (such as occurs when all
robots start from the same location). Second, an approximation is introduced
to solve the more general problem in which the initial pose of robots is
not known a priori (such as occurs when the robots start from widely separated
locations). In this latter case, it is assumed that pairs of robots will
eventually encounter one another, thereby determining their relative pose.
This relative attitude is used to initialize the filter, and subsequent
observations from both robots are combined into a common map. Third and
finally, a method is introduced to integrate observations collected prior
to the first robot encounter, using the notion of a virtual robot travelling
backwards in time. This novel approach allows one to integrate all data
from all robots into a single common map.