  Active SLAM is the task of actively planning robot paths while simultaneously
building a map and localizing within. Existing work has focused on planning
paths with occupancy grid maps, which do not scale well and suffer from long
term drift. This work proposes a Topological Feature Graph (TFG) representation
that scales well and develops an active SLAM algorithm with it. The TFG uses
graphical models, which utilize independences between variables, and enables a
unified quantification of exploration and exploitation gains with a single
entropy metric. Hence, it facilitates a natural and principled balance between
map exploration and refinement. A probabilistic roadmap path-planner is used to
generate robot paths in real time. Experimental results demonstrate that the
proposed approach achieves better accuracy than a standard grid-map based
approach while requiring orders of magnitude less computation and memory
resources.
