AI RESEARCH
VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments
arXiv CS.CV
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ArXi:2605.30506v1 Announce Type: cross Global localization in geometrically aliased, quasi-static environments such as grocery s, offices, schools, and hospitals poses a significant challenge for mobile robots. Grocery s 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.