Robust Identification of Visual Markers Under Boundary Occlusion Condition Visual markers are widely used in indoor markerbased positioning systems to achieve higher speed and accurate positioning performance. However, the classic mark identification methods have certain limitations when encountering complex conditions. Especially, if the markerxe2x80x99s boundary is blocked, it is almost impossible to identify the marker. In this paper, we redefine the identification task to a classification task based on CNN method. We also do some image transformations to create the dataset for our task. We train our dataset by transfer learning based on Googlexe2x80x99s InceptionV3 CNN model. The experimental results show that the classification method can handle the boundary occlusion problem well, which is also proved to be useful for other complex conditions.

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.