Abstract
In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method.
Highlights
Indoor positioning cannot use the GNSS (Global Navigation Satellite System) due to the obstruction of the GPS (Global Positioning System) signal by the building [1]
In the actual physical world, a large number of consistent backgrounds are difficult to be filled with logos and pictures, which leads to the studies of image feature extraction and matching in such sparse feature point scenes to ensure the accuracy of indoor positioning and navigation
In feature extraction of foreground region, Hessian matrix and integral calculation template are introduced, and the process of Gaussian filtering in SIFT is replaced by several addition and subtraction operations
Summary
Indoor positioning cannot use the GNSS (Global Navigation Satellite System) due to the obstruction of the GPS (Global Positioning System) signal by the building [1]. In the actual physical world, a large number of consistent backgrounds are difficult to be filled with logos and pictures, which leads to the studies of image feature extraction and matching in such sparse feature point scenes to ensure the accuracy of indoor positioning and navigation. This paper proposes a feature extraction method ARAC (Adaptive Region Adjustment based on Consistency) to support high-precision positioning and navigation in response to the changes in the image due to different conditions such as shooting position, angle and illumination. This method is based on the free and cross-platform text editor, Visual Studio Code, and the crossplatform computer vision library, Open CV (Open source Computer Vision library) issued under the BSD license (Berkeley Software Distribution) and uses python for programming to achieve feature extraction in a specific environment.
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