基于LLM增强先验的实时室内物体SLAM
Real-Time Indoor Object SLAM with LLM-Enhanced Priors
AI总结：
针对物体级SLAM因观测稀疏导致的优化约束不足问题，提出利用LLM提供物体尺寸和朝向常识作为先验因子，在TUM RGB-D和3RScan上建图精度提升36.8%，实现实时物体SLAM。
摘要：
物体级同时定位与建图（SLAM）通过整合语义信息实现高层场景理解，但面临因观测稀疏而导致的优化约束不足的挑战。先前的工作利用常识知识引入额外约束，但获取此类先验传统上劳动密集且缺乏跨不同物体类别的泛化能力。我们通过利用大型语言模型（LLMs）提供物体几何属性（具体为尺寸和朝向）的常识知识，作为基于图的SLAM框架中的先验因子，来解决这一局限。这些先验在物体观测有限的初始阶段尤其有益。我们实现了一个整合这些先验的完整流程，在稀疏物体级特征上实现了鲁棒的数据关联，并实现了实时物体SLAM。我们的系统在TUM RGB-D和3RScan数据集上进行了评估，相较于最新基线，建图精度提升了36.8%。此外，我们在补充视频中展示了真实世界实验，证明了其实时性能。
英文摘要：
Object-level Simultaneous Localization and Mapping (SLAM), which incorporates semantic information for high-level scene understanding, faces challenges of under-constrained optimization due to sparse observations. Prior work has introduced additional constraints using commonsense knowledge, but obtaining such priors has traditionally been labor-intensive and lacks generalizability across diverse object categories. We address this limitation by leveraging large language models (LLMs) to provide commonsense knowledge of object geometric attributes, specifically size and orientation, as prior factors in a graph-based SLAM framework. These priors are particularly beneficial during the initial phase when object observations are limited. We implement a complete pipeline integrating these priors, achieving robust data association on sparse object-level features and enabling real-time object SLAM. Our system, evaluated on the TUM RGB-D and 3RScan datasets, improves mapping accuracy by 36.8\% over the latest baseline. Additionally, we present real-world experiments in the supplementary video, demonstrating its real-time performance.
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