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Information × Registration Number 2119U006532, Article popup.category Препринт Title Semantic segmentation for visual indoor localization (AI translated) popup.author Kaminskyi YuriiKaminskyi Yurii popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1329 popup.publisher Description The problem of visual localization and navigation in the 3D environment is a key to solving a vast variety of practical tasks. For example in robotics, where the machine is required to locate itself on the 3D map and steer to a specific location. Another example is a personal assistant in the form of a mobile phone or smart glasses that uses augmented reality techniques to navigate the user seamlessly in large indoor spaces such as airports, hospitals, shopping malls or office buildings. The purpose of this work was to improve the performance of the InLoc localization pipeline that gives state-of-the-art results for indoor visual localization problem. That was done by developing relevant semantic features. Namely, we introduce a variety of features as a result of two different segmentation models: Mask R-CNN and CSAIL. We evaluate the quality of generated features and add the features of the better performing model into the InLoc localization pipeline. With the introduced features we improved the performance of the InLoc localization pipeline and introduced approaches for further research. popup.nrat_date 2025-11-05 Close