S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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Indoor Visual Positioning Method Based on Image Features

Xun Liu, He Huang, and Bo Hu

(Received July 21, 2021; Accepted October 25, 2021)

Keywords: indoor visual positioning, ORB feature, bag-of-visual-words model, term frequency–inverse document frequency, efficient perspective-n-point

In this study, we propose an indoor visual positioning method based on image features. RGB-D camera data are used to establish an image database used for positioning. The 3D coordinates of pixels are obtained from an RGB image and depth information, and then the oriented fast and rotated brief (ORB) features of the image are extracted. The bag-of-visual-words model is used in combination with the K-means algorithm and a k-dimensional tree structure to classify storage and expressions in the dictionary. In the positioning process, the positioning image is obtained by a camera with known parameters, and the term frequency–inverse document frequency model is used to achieve image feature indexing to match the most similar image. Finally, using the matching feature points in the image, an efficient perspective-n-point method and bundle adjustment method are used to calculate the camera pose information on the positioning image to complete indoor positioning. Experiments on real scenes verify the feasibility of the proposed method and its positioning accuracy. The results presented in this study provide a useful reference in the research and application of vision-based indoor positioning.

Corresponding author: He Huang




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