A Semantic Navigation Framework for Multi-Floor Building Environment

Autonomous mobile robot navigation in a multi-floor building is a complex task requiring various components: planning, recognition, and localization. Despite the significant progress, an essential issue in a multi-floor environment is to endow the mobile robot with autonomous navigation inside the building via the elevator. Our proposed neuro-inspired cognitive framework provides an efficient solution to this problem based on semantic navigation. In this paper, we utilize three components of our proposed framework, which are the semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module. The SMF uses Triplet Ontological Semantic Model (TOSM) to build the semantic models of the environment. The SIP module includes active environment perception components, and the SAN module contains a behavior planner and behavior database. The development, integration, and interaction among these components based on semantic understanding is the major contribution of our proposed framework. The experimental results demonstrate that our framework effectively enables the mobile robot to move to different floors using the elevator autonomously.