VLM-GLoc：视觉语言模型增强的蒙特卡洛定位，用于杂乱准静态环境中的鲁棒语义全局定位
VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments
AI总结：
提出VLM-GLoc方法，利用开放词汇视觉语言模型作为统一语义观测前端，通过逆语义提议机制和文本到地图检索，在几何模糊和语义歧义的准静态环境中实现鲁棒全局定位。
摘要：
在几何模糊的准静态环境（如杂货店、办公室、学校和医院）中，全局定位对移动机器人构成重大挑战。具有平行过道和长尾产品分布的杂货店，以及具有重复家具（如椅子、桌子、显示器和门）的办公室和实验室，是常见的室内环境，存在几何甚至语义歧义。传统方法要么依赖独特的几何特征，要么依赖特定领域的视觉管道，这些方法难以处理长尾语义分布和瞬态视觉杂乱。我们提出VLM-GLoc，一种分层语义蒙特卡洛定位（MCL）方法，利用开放词汇视觉语言模型（VLM）作为统一语义观测前端。我们假设VLM具有三重优势：（1）提取高度判别性的丰富文本特征，（2）对模糊或动态对象进行隐式质量过滤，（3）针对数据增强的持久性推理。我们引入一种逆语义提议机制，通过文本到地图检索播种粒子。在两个具有不同特征的真实世界环境和两个不同平台上进行评估：一个3500平方英尺的杂货店（使用手机）和一个3700平方英尺的实验室空间（使用四足机器人），VLM-GLoc分别实现了70%和74%的全局定位成功率，显著优于传统的纯几何和特定领域基线方法。
英文摘要：
Global localization in geometrically aliased, quasi-static environments such as grocery stores, offices, schools, and hospitals poses a significant challenge for mobile robots. Grocery stores with parallel aisles and a long tailed distribution of products, as well as offices and labs with repetitive furniture such as chairs, desks, monitors, and doors, exemplify common indoor environments that present geometric and even semantic ambiguity. Traditional approaches rely either on distinct geometric features or on domain-specific vision pipelines that struggle with long-tail semantic distributions and transient visual clutter. We present VLM-GLoc, a method for hierarchical semantic Monte Carlo Localization (MCL) that leverages open-vocabulary Vision-Language Models (VLMs) as a unified semantic observation front-end. We hypothesize a three-fold benefit from VLMs: (1) extracting highly discriminative rich text features, (2) implicit quality filtering of blurry or dynamic objects, and (3) permanence reasoning for targeted data augmentation. We introduce an inverse semantic proposal mechanism that seeds particles via text-to-map retrieval. Evaluated across two real-world environments with different characteristics and two different platforms: a 3,500 sq. ft. grocery store with a cellphone and a 3,700 sq. ft. lab space with a quadruped, VLM-GLoc achieves 70% and 74% global localization success respectively, substantially outperforming traditional geometry-only and domain-specific baselines.
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。
