Indoor localization is one of the crucial enablers for deployment of service robots. Although several successful techniques for indoor localization have been proposed in the past, the majority of them relies on maps generated based on data gathered with the same sensor modality that is 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 with multiple real-world experiments and demonstrate that it has the robustness and accuracy required for reliable indoor navigation.
Visual cognition of the indoor environment can benefit from the spatial layout estimation, which is to represent an indoor scene with a 2D box on a monocular image. In this paper, we propose to fully exploit the edge and semantic information of a room image for layout estimation. More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image. We combine these two network predictions in a scoring function to evaluate the quality of the layouts, which are generated by ray sampling and from a predefined layout pool. Guided by the scoring function, we apply a novel refinement strategy to further optimize the layout hypotheses. Experimental results show that the proposed network can yield accurate estimates of edge maps and semantic labels. By fully utilizing the two different types of labels, the proposed method achieves state-of-the-art layout estimation performance on benchmark datasets. The main contribution of this paper is an extended Kalman filter (EKF)based algorithm for estimating the 6 DOF pose of a camera using monocular images of an indoor environment. In contrast to popular visual simultaneous localisation and mapping algorithms, the technique proposed relies on a pre-built map represented as an unsigned distance function of the ground plane edges. Images from the camera are processed using a Convolutional Neural Network (CNN)to extract a ground plane edge image. Pixels that belong to these edges are used in the observation equation of the EKF to estimate the camera location. Use of the CNN makes it possible to extract ground plane edges under significant changes to scene illumination. The EKF framework lends itself to use of a suitable motion model, fusing information from any other sensors such as wheel encoders or inertial measurement units, if available, and rejecting spurious observations. A series of experiments are presented to demonstrate the effectiveness of the proposed technique. With the popularity of the hand devices and intelligent agents, many aimed to explore machine's potential in interacting with reality. Scene understanding, among the many facets of reality interaction, has gained much attention for its relevance in applications such as augmented reality (AR). Scene understanding can be partitioned into several sub tasks (i.e., layout estimation, scene classification, saliency prediction, etc). In this paper, we propose a deep learning-based approach for estimating the layout of a given indoor image in real-time. Our method consists of a deep fully convolutional network, a novel layout-degeneration augmentation method, and a new training pipeline which integrate an adaptive edge penalty and smoothness terms into the training process. Unlike previous deep learning-based methods that depend on post-processing refinement (e.g., proposal ranking and optimization), our method motivates the generalization ability of the network and the smoothness of estimated layout edges without deploying postprocessing techniques. Moreover, the proposed approach is time-efficient since it only takes the model one forward pass to render accurate layouts. We evaluate our method on LSUN Room Layout and Hedau dataset and obtain estimation results comparable with the state-of-the-art methods. The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work. Existing solutions to this problem largely rely on hand-crafted features and vanishing lines, and they often fail in highly cluttered indoor scenes. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: (1) coarse layout estimation; and (2) fine layout localization. In the first stage, we adopt a fully convolutional neural network (FCN) to obtain a coarse-scale room layout estimate that is close to the ground truth globally. The proposed FCN combines the layout contour property and the surface property so as to provide a robust estimation in the presence of cluttered objects. In the second stage, we formulate an optimization framework that enforces several constraints such as layout contour straightness, surface smoothness and geometric constraints for layout detail refinement. Our proposed system offers the state-of-the-art performance on two commonly used benchmark datasets.