Abstract
The smart city is an important direction for the development of the highly information-based city, and indoor navigation and positioning technology is an important basis for the realization of an intelligent city. In recent years, indoor positioning technology mainly relies on WiFi, radio frequency identification (RFID), Bluetooth, and so on. Yet, the implementation of the above method requires the relevant equipment to be laid out in advance, and it is only suitable for indoor positioning with low accuracy requirements owing to interference and fading of the signal. The visual-based positioning technology can achieve high-precision positioning in enclosed, semienclosed, and multiwalled indoor environments with strong electromagnetic interference by means of epipolar geometry and image matching. The visual-based indoor positioning mostly uses the random sample consensus (RANSAC) algorithm to estimate the fundamental matrix to acquire the user’s relative position. The traditional RANSAC algorithm determines the set of inliers by artificially setting a threshold to estimate the model. However, since the selection of the threshold depends on experience and prior knowledge, the reliability of the positioning results is not robust. Therefore, in order to improve the universality of the algorithm in indoor environments, this paper proposed an improved RANSAC algorithm based on the adaptive threshold and evaluated the real-time and accuracy of the algorithm by using an open-source image library. Results of the experiment show that the algorithm is more accurate than the traditional RANSAC algorithm in an enclosed and semienclosed multiwalled indoor environment, with fewer iterations.
Highlights
Smart city with its highly digital and intelligent features has been widely concerned with various industries; indoor positioning and navigation technology are the fundamental part of the realization of smart city
To solve the problem of time consumption and unreliable results of fundamental matrix estimation caused by the random sample consensus (RANSAC) threshold setting, this paper proposed an adaptive thresholding algorithm to replace the traditional pervasive algorithm for the purpose of optimizing the results of the fundamental matrix and combined with the decomposition of the fundamental matrix to improve the localization results
Epipolar Geometry and Fundamental Matrix e vision-based indoor positioning method utilizes the epipolar constraint between the query image and the database image to determine the relative position of the query camera and the database camera. is relative relationship has nothing to do with the scene structure, but only with the internal and external parameters of the camera
Summary
Smart city with its highly digital and intelligent features has been widely concerned with various industries; indoor positioning and navigation technology are the fundamental part of the realization of smart city. In the literature [17], a method for precise indoor vision positioning of smartphone-based on a single image was proposed, which used PROSAC algorithm (an improved algorithm of RANSAC) to optimize the matching results of the correspondence. Vision-based indoor positioning algorithms have typically used RANSAC and its deformations to estimate the fundamental matrix. 2. Epipolar Geometry and Fundamental Matrix e vision-based indoor positioning method utilizes the epipolar constraint between the query image and the database image to determine the relative position of the query camera and the database camera. When a point in the space is projected onto two different image planes, an image point is generated on two image planes, respectively, and there will be some corresponding relationships between the two image points, which is called epipolar constraint Under this epipolar constraint, the positional relationship between the query camera and the database camera can be represented by the rotation matrix R and the transfer vector t. E classical fundamental matrix estimation methods are the seven-point method and the eight-point method
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