IRIS-SLAM：统一的地理实例表示用于鲁棒的语义定位与建图
IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping
AI总结：
IRIS-SLAM通过统一的几何-实例表示提升语义SLAM的鲁棒性，解决传统方法在语义理解和闭环检测上的不足。
摘要：
几何基础模型显著推动了密集几何SLAM的发展，但现有系统常缺乏深度语义理解和鲁棒闭环能力。同时，当代语义建图方法常受限于解耦架构和脆弱的数据关联。我们提出IRIS-SLAM，一种新的RGB语义SLAM系统，利用从实例扩展基础模型中衍生的统一几何-实例表示。通过扩展几何基础模型，同时预测密集几何和跨视角一致的实例嵌入，我们实现了语义协同的关联机制和实例引导的闭环检测。我们的方法有效利用视角无关的语义锚点，弥合几何重建与开放词汇建图之间的差距。实验结果表明，IRIS-SLAM在地图一致性和宽基线闭环可靠性方面显著优于现有方法。
英文摘要：
Geometry foundation models have significantly advanced dense geometric SLAM, yet existing systems often lack deep semantic understanding and robust loop closure capabilities. Meanwhile, contemporary semantic mapping approaches are frequently hindered by decoupled architectures and fragile data association. We propose IRIS-SLAM, a novel RGB semantic SLAM system that leverages unified geometric-instance representations derived from an instance-extended foundation model. By extending a geometry foundation model to concurrently predict dense geometry and cross-view consistent instance embeddings, we enable a semantic-synergized association mechanism and instance-guided loop closure detection. Our approach effectively utilizes viewpoint-agnostic semantic anchors to bridge the gap between geometric reconstruction and open-vocabulary mapping. Experimental results demonstrate that IRIS-SLAM significantly outperforms state-of-the-art methods, particularly in map consistency and wide-baseline loop closure reliability.
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