PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings
This addresses navigation problems in functional buildings for robotics or AI systems, representing a domain-specific incremental advance.
The paper tackles the challenge of language-driven embodied navigation in functional buildings with highly similar features by proposing PM-Nav, which uses priori-maps and hierarchical prompts, resulting in average improvements of 511% to 1175% over baselines in simulation and real-world tests.
Existing language-driven embodied navigation paradigms face challenges in functional buildings (FBs) with highly similar features, as they lack the ability to effectively utilize priori spatial knowledge. To tackle this issue, we propose a Priori-Map Guided Embodied Navigation (PM-Nav), wherein environmental maps are transformed into navigation-friendly semantic priori-maps, a hierarchical chain-of-thought prompt template with an annotation priori-map is designed to enable precise path planning, and a multi-model collaborative action output mechanism is built to accomplish positioning decisions and execution control for navigation planning. Comprehensive tests using a home-made FB dataset show that the PM-Nav obtains average improvements of 511\% and 1175\%, and 650\% and 400\% over the SG-Nav and the InstructNav in simulation and real-world, respectively. These tremendous boosts elucidate the great potential of using the PM-Nav as a backbone navigation framework for FBs.