Vision-based Indoor Positioning Method By Joint Using 2D Images and 3D Point Cloud Map

2D image-based and 3D structure-based are the most two popular vision-based indoor positioning methods. Though 2D method has better efficiency for image retrieval, the number of stored images is usually too many to achieve fast 2D-2D matching, and it cannot obtain high positioning accuracy either. In contrast, 3D method has smaller database and can achieve much higher positioning accuracy with the help of 3D information. However, it needs to construct 3D point cloud map of the positioning environment and spend too much time on 2D-3D matching. Therefore, in order to improve the positioning accuracy and the retrieval efficiency, we propose a vision-based indoor positioning method by joint use 2D images and 3D point cloud map in this paper. In offline stage, we select key images into the offline database based on the 3D point cloud map that is built by RGB-D simultaneous localization and mapping (SLAM), and establish the mapping relation between 2D image local feature points and 3D points. In online stage, we utilize the rough-fine matching strategy to obtain 2D-3D matching pairs between local feature points of the user query image and 3D points in the 3D point cloud map. The user position is finally estimated by the efficient perspective-n-point (EPnP) algorithm. Performance analysis and simulation results indicate that our proposed method can not only achieve high positioning accuracy but also speed up the matching process.