  In this paper, we propose an integrated framework for the autonomous robotic
exploration in indoor environments. Specially, we present a hybrid map, named
Semantic Road Map (SRM), to represent the topological structure of the explored
environment and facilitate decision-making in the exploration. The SRM is built
incrementally along with the exploration process. It is a graph structure with
collision-free nodes and edges that are generated within the sensor coverage.
Moreover, each node has a semantic label and the expected information gain at
that location. Based on the concise SRM, we present a novel and effective
decision-making model to determine the next-best-target (NBT) during the
exploration. The model concerns the semantic information, the information gain,
and the path cost to the target location. We use the nodes of SRM to represent
the candidate targets, which enables the target evaluation to be performed
directly on the SRM. With the SRM, both the information gain of a node and the
path cost to the node can be obtained efficiently. Besides, we adopt the
cross-entropy method to optimize the path to make it more informative. We
conduct experimental studies in both simulated and real-world environments,
which demonstrate the effectiveness of the proposed method.
