Abstract
Recently, the demand for location-based services using mobile devices in indoor spaces without a global positioning system (GPS) has increased. However, to the best of our knowledge, solutions that are fully applicable to indoor positioning and navigation and ensure real-time mobility on mobile devices, such as global navigation satellite system (GNSS) solutions, cannot achieve remarkable researches in indoor circumstances. Indoor single-shot image positioning using smartphone cameras does not require a dedicated infrastructure and offers the advantages of low price and large potential markets owing to the popularization of smartphones. However, existing methods or systems based on smartphone cameras and image algorithms encounter various limitations when implemented in indoor environments. To address this, we designed an indoor visual positioning system for mobile devices that can locate users in indoor scenes. The proposed method uses a smartphone camera to detect objects through a single image in a web environment and calculates the location of the smartphone to find users in an indoor space. The system is inexpensive because it integrates deep learning and computer vision algorithms and does not require additional infrastructure. We present a novel method of detecting 3D model objects from single-shot RGB data, estimating the 6D pose and position of the camera and correcting errors based on voxels. To this end, the popular convolutional neural network (CNN) is improved by real-time pose estimation to handle the entire 6D pose estimate the location and direction of the camera. The estimated position of the camera is addressed to a voxel to determine a stable user position. Our VPS system provides the user with indoor information in 3D AR model. The voxel address optimization approach with camera 6D position estimation using RGB images in a mobile web environment outperforms real-time performance and accuracy compared to current state-of-the-art methods using RGB depth or point cloud.
Highlights
Multi-usage public facilities or large crowded markets without global positioning system (GPS) functionality fail to navigation services
The pose estimation error of the improved single-shot deep convolutional neural network (CNN) is proportional to the x, y, and z coordinates of the object center and the rotational pitch, yaw, and roll values of the object, and it is proportional to the origin coordinates (0, 0, 0) of the camera
Our method consists of a network module and an algorithm module, and it is computed using our equation in the algorithm, and the measurement uncertainty in our system is proportional to the estimations of network-specific
Summary
Multi-usage public facilities or large crowded markets without GPS functionality fail to navigation services. Machine learning and deep learning methods are applied without sensors for location recognition. Visual positioning system information, which is more innovative than navigation technology obtained using GPS information, resonates with people’s lifestyles globally. VPS allows users to use their mobile cameras to visually grasp their surroundings and directions in places where GPS services are difficult, such as indoor spaces [2]. These techniques can accurately recognize a location of user through learning only by collecting images from mobile camera. Among recent object pose estimation approaches available for VPS, methods which are counting on depth maps with color images have shown excellent performance [3–5]. Among the indoor positioning methods, though a QRcode method with screenshot have a high accuracy, it has a problem which the user’s position should be determined approximately
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