Evaluating General-Purpose Multimodal LLM for Indoor Localization on Floorplans

Sammanfattning

This thesis investigates whether a general-purpose multimodal large language model (LLM) can be used for indoor localization by estimating the position of a query image on a floorplan without task-specific training or prior environmental knowledge. Indoor localization remains a challenging problem because many existing solutions depend on dedicated infrastructure or prior mapping. A general-purpose multimodal LLM is therefore interesting to investigate because it may offer a low-setup alternative that uses visual and spatial reasoning rather than specialized localization hardware or training data. An experimental study was conducted in a controlled office environment where the model was evaluated under varying conditions, including different prompt strategies, image types, number of query images, image quality and environmental settings. Localization performance was measured using Euclidean error distance, and both accuracy and consistency were analyzed across 432 test cases. The results show that the model is capable of producing reasonable localization estimates in some cases, with a mean error of about 9 meters, but performance is highly variable and lacks consistency. Differences between prompt strategies were minimal, while environmental factors and image conditions had a more noticeable impact. The model demonstrated an ability to interpret structural and semantic features from images, but often struggled to distinguish between similar locations on the floorplan. Coherent reasoning explanations did not consistently correspond to accurate spatial predictions. Overall, the findings indicate that the tested general-purpose multimodal LLM has potential for approximate, area-level indoor localization with low setup requirements, but was not reliable enough for precise positioning. The approach is therefore more suitable as a supportive component in a localization system than as a standalone indoor positioning solution.