Abstract
Indoor visual positioning is a key technology in a variety of indoor location services and applications. The particular spatial structures and environments of indoor spaces is a challenging scene for visual positioning. To address the existing problems of low positioning accuracy and low robustness, this paper proposes a precision single-image-based indoor visual positioning method for a smartphone. The proposed method includes three procedures: First, color sequence images of the indoor environment are collected in an experimental room, from which an indoor precise-positioning-feature database is produced, using a classic speed-up robust features (SURF) point matching strategy and the multi-image spatial forward intersection. Then, the relationships between the smartphone positioning image SURF feature points and object 3D points are obtained by an efficient similarity feature description retrieval method, in which a more reliable and correct matching point pair set is obtained, using a novel matching error elimination technology based on Hough transform voting. Finally, efficient perspective-n-point (EPnP) and bundle adjustment (BA) methods are used to calculate the intrinsic and extrinsic parameters of the positioning image, and the location of the smartphone is obtained as a result. Compared with the ground truth, results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm. In addition, experiments show that the proposed method is more robust and efficient than the baseline method in a real scene. In the case where sufficient indoor textures are present, it has the potential to become a low-cost, precise, and highly available indoor positioning technology.
Highlights
Positioning is one of the core technologies used in location-based services, augmented reality (AR), internet of everything, customer analytics, guiding vulnerable people, robotic navigation, and artificial intelligence applications [1,2,3,4,5]
Most state-of-the-art methods [1,2,4,6,14,15,16,17,18] rely on local features such as SIFT or speed-up robust features (SURF) [21,22] to solve the problem of image-based localization
We first modify and extend a method for indoor positioning feature database establishment based on existing classical matching algorithms and strategies; our main aim is to use an epipolar constraint based on the fundamental matrix and a matching image screening strategy based on image overlap during construction of the positioning feature database
Summary
Positioning is one of the core technologies used in location-based services, augmented reality (AR), internet of everything, customer analytics, guiding vulnerable people, robotic navigation, and artificial intelligence applications [1,2,3,4,5]. In image invariant local features-based indoor visual positioning approaches, according to the research content and characteristics of the visual positioning technology, there are two key problems at the algorithm level: (1) how to calculate the precise spatial pose of the positioning image robustly and rapidly; and (2) how to generate a high-precision positioning feature library. In the case of no new observation sources (such as WIFI, Ins, magnetic, etc.) being available, there exist many problems to be solved in these two aspects This makes the application of visual positioning in indoor scenes more difficult than in outdoor environments, especially for the problem of image-feature mismatch caused by the lack of decorative texture or texture repetition in indoor scenes.
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