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Indoor localization is one of the crucial enablers for deployment of servicerobots. Although several successful techniques for indoor localization havebeen proposed, the majority of them relies on maps generated from data gatheredwith the same sensor modality used for localization. Typically, tedious laborby experts is needed to acquire this data, thus limiting the readiness of thesystem as well as its ease of installation for inexperienced operators. In thispaper, we propose a memory and computationally efficient monocular camera-basedlocalization system that allows a robot to estimate its pose given anarchitectural floor plan. Our method employs a convolutional neural network topredict room layout edges from a single camera image and estimates the robotpose using a particle filter that matches the extracted edges to the givenfloor plan. We evaluate our localization system using multiple real-worldexperiments and demonstrate that it has the robustness and accuracy requiredfor reliable indoor navigation.

Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network