Abstract:
The main contribution of this paper is a new
SLAM algorithm for the mapping of large scale environments
by combining local maps. The local maps can be generated
by traditional Extended Kalman Filter (EKF) based SLAM.
Relationships between the locations of the landmarks in the local
map are then extracted and used in an Extended Information
Filter (EIF) to build a global map. An important feature is that
the information matrix for the global map is exactly sparse,
leading to significant computational advantages. This paper thus
presents an algorithm that combines the advantages of both the
existing local map joining SLAM algorithms, which reduces the
linearization error in EKF SLAM and allows computationally
demanding global map fusion to be scheduled off-line, and
the Decoupled SLAM (D-SLAM) algorithm, which provides an
efficient strategy for building large maps using relative location
information. The effectiveness of the new algorithm is illustrated
through computer simulations.
