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This paper introduces PM-Nav, a novel embodied navigation framework designed for functional buildings (FBs) by leveraging priori spatial knowledge. The approach transforms environmental maps into semantic priori-maps and employs a hierarchical chain-of-thought prompt template to enable precise path planning. Experimental results on a custom FB dataset demonstrate significant improvements over existing methods like SG-Nav and InstructNav in both simulation and real-world scenarios.
Achieve up to 11x navigation performance gains in functional buildings by explicitly encoding and exploiting a priori spatial knowledge.
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.
