用统一语义几何表示提升定位与建图的鲁棒性
IRIS-SLAM: Unified Geo-Instance Representations for Robust Semantic Localization and Mapping
- 基于扩展的基础模型，同时预测稠密几何和跨视角一致的实例嵌入
- 在宽基线回环检测上表现更优，地图一致性显著提升
- 适合需要高鲁棒性的开放词汇语义建图场景
几何基础模型显著推动了密集几何SLAM的发展，但现有系统往往缺乏深层语义理解与鲁棒的回环闭合能力。同时，当前语义建图方法常受解耦架构和脆弱数据关联限制。我们提出IRIS-SLAM，一种新型RGB语义SLAM系统，利用来自实例扩展基础模型的统一几何-实例表示。通过将几何基础模型扩展为同时预测稠密几何与跨视图一致的实例嵌入，实现语义协同的数据关联与实例引导的回环检测。该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映射之间的差距。实验表明，IRIS-SLAM显著优于现有先进方法，尤其在地图一致性和宽基线回环闭合可靠性方面表现突出。
原文摘要 · Abstract (English)
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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