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Open AccessArticle

A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction

1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20A, Datun Road, Chaoyang District, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2572; https://doi.org/10.3390/rs11212572
Received: 27 August 2019 / Revised: 16 October 2019 / Accepted: 30 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue Mobile Mapping Technologies)
High-accuracy indoor positioning is a prerequisite to satisfy the increasing demands of position-based services in complex indoor scenes. Current indoor visual-positioning methods mainly include image retrieval-based methods, visual landmarks-based methods, and learning-based methods. To better overcome the limitations of traditional methods such as them being labor-intensive, of poor accuracy, and time-consuming, this paper proposes a novel indoor-positioning method with automated red, green, blue and depth (RGB-D) image database construction. First, strategies for automated database construction are developed to reduce the workload of manually selecting database images and ensure the requirements of high-accuracy indoor positioning. The database is automatically constructed according to the rules, which is more objective and improves the efficiency of the image-retrieval process. Second, by combining the automated database construction module, convolutional neural network (CNN)-based image-retrieval module, and strict geometric relations-based pose estimation module, we obtain a high-accuracy indoor-positioning system. Furthermore, in order to verify the proposed method, we conducted extensive experiments on the public indoor environment dataset. The detailed experimental results demonstrated the effectiveness and efficiency of our indoor-positioning method. View Full-Text
Keywords: visual positioning; indoor scenes; automated database construction; image retrieval visual positioning; indoor scenes; automated database construction; image retrieval
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MDPI and ACS Style

Wang, R.; Wan, W.; Di, K.; Chen, R.; Feng, X. A High-Accuracy Indoor-Positioning Method with Automated RGB-D Image Database Construction. Remote Sens. 2019, 11, 2572.

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