In this paper, we present an information-based
approach to select a reduced number of landmarks and poses
for a robot to localize itself and simultaneously build an
accurate map. We develop an information theoretic algorithm
to efficiently reduce the number of landmarks and poses in
a SLAM estimate without compromising the accuracy of the
estimated trajectory. We also propose an incremental version of
the reduction algorithm which can be used in SLAM framework
resulting in information based reduced landmark SLAM. The
results of reduced landmark based SLAM algorithm are shown
on Victoria park dataset and a Synthetic dataset and are
compared with standard graph SLAM algorithm.
We demonstrate a reduction of 40-50% in the number of
landmarks and around 55% in the number of poses with
minimal estimation error as compared to standard SLAM
algorithm.