  As the robot explores the environment, the map grows over time in the
simultaneous localization and mapping (SLAM) system, especially for the large
scale environment. The ever-growing map prevents long-term mapping. In this
paper, we developed a compact cognitive mapping approach inspired by
neurobiological experiments. Inspired from neighborhood cells, neighborhood
fields determined by movement information, i.e. translation and rotation, are
proposed to describe one of distinct segments of the explored environment. The
vertices and edges with movement information below the threshold of the
neighborhood fields are avoided adding to the cognitive map. The optimization
of the cognitive map is formulated as a robust non-linear least squares
problem, which can be efficiently solved by the fast open linear solvers as a
general problem. According to the cognitive decision-making of familiar
environments, loop closure edges are clustered depending on time intervals, and
then parallel computing is applied to perform batch global optimization of the
cognitive map for ensuring the efficiency of computation and real-time
performance. After the loop closure process, scene integration is performed, in
which revisited vertices are removed subsequently to further reduce the size of
the cognitive map. A monocular visual SLAM system is developed to test our
approach in a rat-like maze environment. Our results suggest that the method
largely restricts the growth of the size of the cognitive map over time, and
meanwhile, the compact cognitive map correctly represents the overall layer of
the environment as the standard one. Experiments demonstrate that our method is
very suited for compact cognitive mapping to support long-term robot mapping.
Our approach is simple, but pragmatic and efficient for achieving the compact
cognitive map.
