Abstract:
This paper presents a novel approach to the Simultaneous
Localisation and Mapping (SLAM) algorithm that
exploits the manlier in which observations are fused into
the global map of the environment to nianage the computational
complexity of tlie 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 niap of the environment
using appropriately formulated constraints between
the coninion feature estimates. This approach is shown to
be effective in reducing the computational complexity of
maintaining the global map estimates as well as improving
the data association process.
