| An efficient probabilistic
algorithm for the concurrent mapping and localization problem that arises
in mobile robotics is presented. The algorithm addresses the problem in
which a team of robots builds a map on-line while simultaneously accommodating
errors in the robots' odometry. At the core of the algorithm is a technique
that combines fast maximum likelihood map growing with a Monte Carlo localizer
that uses particle representations. The combination of both yields an on-line
algorithm that can cope with large odometric errors typically found when
mapping environments with cycles. The algorithm can be implemented in a
distributed manner on multiple robot platforms, enabling a team of robots
to cooperatively generate a single map of their environment. Finally, an
extension is described for acquiring three-dimensional maps, which capture
the structure and visual appearance of indoor environments in three dimensions. |
