面向SLAM的开放词汇在线语义建图
Open-Vocabulary Online Semantic Mapping for SLAM
AI总结：
本文提出OVO，一种基于CLIP的开放词汇在线三维语义建图流程，具有低计算和内存开销，优于现有方法，并首次集成到SLAM后端实现端到端建图。
摘要：
本文提出了一种开放词汇的在线三维语义建图流程，我们以其缩写OVO表示。给定一系列带位姿的RGB-D帧，我们检测并跟踪三维片段，并使用CLIP向量对其进行描述。这些向量通过一种新颖的CLIP合并方法从观察到它们的视角计算得出。值得注意的是，我们的OVO相比离线基线具有显著更低的计算和内存占用，同时在分割指标上也优于离线和在线方法。除了更优的分割性能，我们还展示了将我们的建图贡献与两个完整的SLAM后端（Gaussian-SLAM和ORB-SLAM2）集成后的实验结果，这是首次使用神经网络合并CLIP描述符，并展示了带闭环的端到端开放词汇在线三维建图。
英文摘要：
This paper presents an Open-Vocabulary Online 3D semantic mapping pipeline, that we denote by its acronym OVO. Given a sequence of posed RGB-D frames, we detect and track 3D segments, which we describe using CLIP vectors. These are computed from the viewpoints where they are observed by a novel CLIP merging method. Notably, our OVO has a significantly lower computational and memory footprint than offline baselines, while also showing better segmentation metrics than offline and online ones. Along with superior segmentation performance, we also show experimental results of our mapping contributions integrated with two different full SLAM backbones (Gaussian-SLAM and ORB-SLAM2), being the first ones using a neural network to merge CLIP descriptors and demonstrating end-to-end open-vocabulary online 3D mapping with loop closure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
