  Indoor localization is one of the crucial enablers for deployment of service
robots. Although several successful techniques for indoor localization have
been proposed, the majority of them relies on maps generated from data gathered
with the same sensor modality used for localization. Typically, tedious labor
by experts is needed to acquire this data, thus limiting the readiness of the
system as well as its ease of installation for inexperienced operators. In this
paper, we propose a memory and computationally efficient monocular camera-based
localization system that allows a robot to estimate its pose given an
architectural floor plan. Our method employs a convolutional neural network to
predict room layout edges from a single camera image and estimates the robot
pose using a particle filter that matches the extracted edges to the given
floor plan. We evaluate our localization system using multiple real-world
experiments and demonstrate that it has the robustness and accuracy required
for reliable indoor navigation.
