Hierarchical 3D Scene Graph Construction and Belief-based Planning for Semantic Navigation

Semantic navigation is a fundamental task for embodiedagents operating in unseen environments, requiring both semantic un-derstanding and long-term decision-making. Recent foundation modelshave empowered agents with rich semantic priors for this task. How-ever, without structured global representations, decision-making oftenfalls back on local observations and greedy strategies, resulting in in-efficient exploration and myopic behaviors, especially in long-distancenavigation. To address these challenges, we propose a zero-shot semanticnavigation framework. Our method incrementally maintains an onlineHierarchical 3D Scene Graph (HSG) to form a multi-granular semantictopology over objects, zones, and regions, serving as a compact stateabstraction for global planning. Building on this memory, we introducea hierarchical belief-based planning framework that fuses semantic pri-ors with exploration evidence on the HSG, and performs finite-horizonrollouts on an HSG-based simulator to explicitly estimate the long-termexpected returns of candidate macro-actions. This enables globally con-sistent decisions and reduces redundant backtracking. Extensive experi-ments in high-fidelity simulation environments across multiple tasks anddatasets demonstrate that our method outperforms existing state-of-the-art methods, particularly in long-distance scenarios, where our approachimproves SR and SPL by an average of 9.4% and 5.0%, respectively.