arXiv:2605.22581v1 Announce Type: cross Abstract: Many public buildings provide floorplans with a "you are here" indicator to help visitors orient themselves. Floorplan localization seeks to computationally replicate this capability by determining where visual observations were captured within a floorplan. However, existing methods typically assume controlled small-scale environments and precise vectorized floorplans, limiting their ability to operate in large-scale buildings and rasterized floorplans. In this work, we present an approach for performing floorplan localization in the wild by gr
Advances in 3D vision and machine learning are enabling more robust and practical indoor localization solutions, moving beyond constrained environments to real-world applications.
Accurate, real-time indoor localization using existing floorplans has widespread implications for logistics, public safety, augmented reality, and large-scale facility management.
The ability to accurately localize within complex indoor environments using readily available, even rasterized, floorplans signifies a major step towards pervasive AI-driven spatial intelligence.
- · Logistics and warehousing companies
- · Public safety and emergency services
- · Augmented reality developers
- · Facility management services
- · Legacy indoor positioning systems
- · Manual navigation methods in large venues
Improved efficiency and safety within large indoor spaces through precise navigation and asset tracking.
Development of new augmented reality applications that seamlessly integrate with real-world indoor environments.
Enhanced AI 'understanding' of human-built spaces, leading to more intelligent robotic automation and personalized spatial experiences.
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Read at arXiv cs.LG