  This paper proposes a novel semantics-aware autonomous exploration model to
handle the long-standing issue: the mainstream RRT (Rapid-exploration Random
Tree) based exploration models usually make the mobile robot switch frequently
between different regions, leading to the excessively-repeated explorations for
the same region. Our proposed semantics-aware model encourages a mobile robot
to fully explore the current region before moving to the next region, which is
able to avoid excessively-repeated explorations and make the exploration
faster. The core idea of semantics-aware autonomous exploration model is
optimizing the sampling point selection mechanism and frontier point evaluation
function by considering the semantic information of regions. In addition,
compared with existing autonomous exploration methods that usually construct
the single-type or 2-3 types of maps, our model allows to construct four kinds
of maps including point cloud map, occupancy grid map, topological map, and
semantic map. To test the performance of our model, we conducted experiments in
three simulated environments. The experiment results demonstrate that compared
to Improved RRT, our model achieved 33.0% exploration time reduction and 39.3%
exploration trajectory length reduction when maintaining >98% exploration rate.
