A novel plane based image registration pipeline with CNN scene parsing Plane is one of the most important element for indoor manmade structural rooms, such as broadcasting studios, open lecture room and empty offices. The existing visual mapping algorithms cannot effectively detect and describe the visual features on an empty large or medium size plane. This article aims to introduce a novel frametoframe registration pipeline based on one mediumsize plane on manmade object instead of multiple planes or small plane patches. By introducing a structural description of reference planar area with its contour data and CNN segmentation information, the proposed approach is able track the pose of camera with high accuracy and robustness in comparison with existing featurebased tracking or dense geometric tracking approaches.

Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network 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 camerabased 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 realworld experiments and demonstrate that it has the robustness and accuracy required for reliable indoor navigation.