通过空间语义关系实现零样本物体导航，避免探索困局。
SSR-ZSON: Zero-Shot Object Navigation via Spatial-Semantic Relations within a Hierarchical Exploration Framework
- 基于分层探索框架，动态生成兼顾语义密度与空间覆盖的视角。
- 在Matterport3D上成功率提升18.5%，路径加权成功率提升0.181。
- 适合需高效零样本导航的机器人系统研发人员参考。
未知环境中零样本物体导航面临两大挑战：语义引导不足导致探索效率低下，环境结构限制导致空间记忆缺失引发局部困局。为此，我们提出SSR-ZSON，一种基于TARE分层探索框架的空间-语义关系零样本导航方法，融合视角生成策略与大语言模型（LLM）全局引导机制。该方法通过优先选择可通行子区域内的高语义密度区域生成视角，最大化空间覆盖并减少无效探索；结合LLM评估语义关联，引导导航至高价值空间，防止局部困顿。在混合Habitat-Gazebo仿真与真实物理平台部署中实现实时运行。在Matterport3D与Habitat-Matterport3D数据集上，相比当前最优方法，成功率（SR）分别提升18.5%和11.2%，路径加权成功率（SPL）分别提升0.181和0.140。
原文摘要 · Abstract (English)
Zero-shot object navigation in unknown environments presents significant challenges, mainly due to two key limitations: insufficient semantic guidance leads to inefficient exploration, while limited spatial memory resulting from environmental structure causes entrapment in local regions. To address these issues, we propose SSR-ZSON, a spatial-semantic relative zero-shot object navigation method based on the TARE hierarchical exploration framework, integrating a viewpoint generation strategy balancing spatial coverage and semantic density with an LLM-based global guidance mechanism. The performance improvement of the proposed method is due to two key innovations. First, the viewpoint generation strategy prioritizes areas of high semantic density within traversable sub-regions to maximize spatial coverage and minimize invalid exploration. Second, coupled with an LLM-based global guidance mechanism, it assesses semantic associations to direct navigation toward high-value spaces, preventing local entrapment and ensuring efficient exploration. Deployed on hybrid Habitat-Gazebo simulations and physical platforms, SSR-ZSON achieves real-time operation and superior performance. On Matterport3D and Habitat-Matterport3D datasets, it improves the Success Rate(SR) by 18.5\% and 11.2\%, and the Success weighted by Path Length(SPL) by 0.181 and 0.140, respectively, over state-of-the-art methods.
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