# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 1 Introduction  
Information obtained from visual positioning systems (VPSs) is more innovative than that obtained from navigation technology, such as global positioning systems (GPSs). Therefore, VPSs are increasingly being used to improve the lifestyles of people worldwide. For instance, in places where GPS service is not available, such as indoor spaces, the VPS can use the user's mobile camera to identify the surroundings and visually determine directions [1].  
In this study, we propose a novel VPS method that determines the position of the camera by matching the pose information of the object estimated using the improved point-graph neural network (GNN) with the pose information of the object in a predefined voxel database. The proposed method uses a sparsely down-sampled point cloud that can be obtained from mobile devices and uses RGB information as feature information. However, unlike images, point clouds are generally sparse and are not evenly distributed on a regular grid. This implies that placing the point cloud on a regular grid generates a non-uniform number of points in the grid cells, which may lead to potential information loss in congested cells or wasted operations in empty cells when the convolution operations of same kernel size are applied to such a grid [2]. Therefore, a GNN was used to structure the point cloud, as the graph can be directly applied to the structure of the point cloud without setting it up regularly. Additionally, GNN can reuse graph edges in all layers andneed not extract the grouped and sampled points repeatedly [3]. The proposed method is a novel VPS based on point-GNN [4] that structures a three-dimensional (3D) point cloud to precisely acquire spatial information and detect objects using a GNN. The user's position can be determined by matching the pose information of the object estimated by the improved point-GNN with the pose information label of the object in the voxel database, which addresses the point cloud in the indoor space in voxel units of a certain size.  
The contributions of this study can be summarized as follows:  
* The proposed method estimates the pose of an object in terms of nine degrees of freedom (DOF) by improving the point-GNN.
* This is a novel method that determines the user's position as a voxel address in indoor space by matching the object pose label in the defined voxel database with the object pose estimated using the user's camera position.  
Our method is a novel method of determining a position in a 3D voxel using the point cloud. The existing positioning method is an image learning-based neural network, and the general method calculates two 3D bounding boxes IoU (Intersection over Union) in seven degrees of freedom with a single rotation (around the z-axis) using a bird's eye view. Our method calculates the rotated IoU around all axes (x, y, z) with nine degrees of freedom (three transformations, three dimensions, three rotations) for pose estimation in 3D space. After calculating the 3D IoU, it is calculated with our formulas and algorithms to determine the position. We improved a new object detection approach using graph neural network on the point cloud and is a novel method of structuring point clouds by addressing 3D space with voxel labeling.  
The remainder of this paper is organized as follows. Section 2 provides a brief overview of related work. Section 3 describes the system architecture and the proposed method in detail. Experiments and performance evaluations are presented in Section 4. Section 5 discusses the inference of the obtained results, and Section 6 summarizes the study conclusions and suggests potential future applications.