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Sensors 2017, 17(9), 1951; https://doi.org/10.3390/s17091951

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

1
,
1
,
1,2,3
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1
and
1,2,3,4,*
1
School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
3
Key Laboratory of Digital Mapping and Land information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
4
Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 18 July 2017 / Revised: 18 August 2017 / Accepted: 21 August 2017 / Published: 24 August 2017
(This article belongs to the Special Issue Low Power Embedded Sensing: Hardware-Software Design and Applications)
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Abstract

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction. View Full-Text
Keywords: geovisualization; outdoor augmented reality; deep learning; object detection; Inertial Measurement Unit geovisualization; outdoor augmented reality; deep learning; object detection; Inertial Measurement Unit
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rao, J.; Qiao, Y.; Ren, F.; Wang, J.; Du, Q. A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization. Sensors 2017, 17, 1951.

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