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Sensors 2016, 16(2), 166; doi:10.3390/s16020166

An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database

1
,
2,†,* , 3,†
and
3,†
1
School of Information Engineering, Chang’an University, Xi’an 710064, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
Xi’an Research Institute of Surveying and Mapping, Xi’an 710054, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Yajing Shen
Received: 1 December 2015 / Revised: 11 January 2016 / Accepted: 18 January 2016 / Published: 28 January 2016
(This article belongs to the Special Issue Sensors for Robots)
View Full-Text   |   Download PDF [3088 KB, uploaded 4 February 2016]   |  

Abstract

In determining position and attitude, vision navigation via real-time image processing of data collected from imaging sensors is advanced without a high-performance global positioning system (GPS) and an inertial measurement unit (IMU). Vision navigation is widely used in indoor navigation, far space navigation, and multiple sensor-integrated mobile mapping. This paper proposes a novel vision navigation approach aided by imaging sensors and that uses a high-accuracy geo-referenced image database (GRID) for high-precision navigation of multiple sensor platforms in environments with poor GPS. First, the framework of GRID-aided vision navigation is developed with sequence images from land-based mobile mapping systems that integrate multiple sensors. Second, a highly efficient GRID storage management model is established based on the linear index of a road segment for fast image searches and retrieval. Third, a robust image matching algorithm is presented to search and match a real-time image with the GRID. Subsequently, the image matched with the real-time scene is considered to calculate the 3D navigation parameter of multiple sensor platforms. Experimental results show that the proposed approach retrieves images efficiently and has navigation accuracies of 1.2 m in a plane and 1.8 m in height under GPS loss in 5 min and within 1500 m. View Full-Text
Keywords: vision navigation; imaging sensor; geo-referenced; image database; multiple sensor-integrated mobile mapping; image retrieval; image matching vision navigation; imaging sensor; geo-referenced; image database; multiple sensor-integrated mobile mapping; image retrieval; image matching
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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Li, Y.; Hu, Q.; Wu, M.; Gao, Y. An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database. Sensors 2016, 16, 166.

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