Abstract:
This paper presents an algorithm for the multi-robot simultaneous
localization and mapping (SLAM) problem with the
robot initial locations completely unknown. Each robot builds
its own local map using the traditional Extended Kalman
Filter (EKF) SLAM algorithm. We provide a new method to
fuse the local maps into a jointly maintained global map by
first transforming the local map state estimate into relative
location information and then conducting the fusion using
the decoupled SLAM (D-SLAM) framework [10]. An efficient
algorithm to find the map overlap and corresponding beacons
across the maps is developed from a point feature based
medical image registration method and the joint compatibility
test. By adding the robot initial pose of each local map into
the global map state, the algorithm shows valuable properties.
Simulation results are provided to illustrate the effectiveness
of the algorithm.
