Abstract:
This paper presents a novel approach to the multi-vehicle
Simultaneous Localisation and Mapping (SLAM) problem
that exploits the manner in which observations are fused
into the global map of the environment to manage the computatidnal
complexity of the algorithm and improve the
data association process. Rather than incorporating every
observation directly into the global map of the environment,
the Constrained Local Submap Filter (CLSF) relies
on creating an independent, local submap of the features in
the immediate vicinity of the vehicle. This local submap is
then periodically fused into the global map of the environment.
This representation has been shown to reduce the
computational complexity of maintaining the global map
estimates as well as improving the data association process.
This paper examines the prospect of applying the
CLSF algorithm to the multi-vehicle SLAM problem.
