An autonomous robot mapping method based on boundary point optimization and map partition exploration

Abstract The proposed methodology is an autonomous mapping technique founded on frontier point optimization and map partition exploration. The objective of this methodology is twofold: first, to reduce the robot’s movement path during autonomous mapping, and second, to minimize repeated exploration of the same area. Initially, candidate exploration points are generated by clustering frontier points, which are then subjected to evaluation, and the optimal target point is ascertained based on the evaluation outcomes, the current map partition information, and the map exploration progress. The validity of the proposed algorithm is substantiated through both simulation and real-world scenarios.