{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:17:51Z","timestamp":1760242671325,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2016,1,28]],"date-time":"2016-01-28T00:00:00Z","timestamp":1453939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41271452","41271431"],"award-info":[{"award-number":["41271452","41271431"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s16020166","type":"journal-article","created":{"date-parts":[[2016,1,28]],"date-time":"2016-01-28T10:31:20Z","timestamp":1453977080000},"page":"166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database"],"prefix":"10.3390","volume":"16","author":[{"given":"Yan","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0866-6678","authenticated-orcid":false,"given":"Qingwu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Meng","family":"Wu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Surveying and Mapping, Xi\u2019an 710054, China"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"Xi\u2019an Research Institute of Surveying and Mapping, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,28]]},"reference":[{"key":"ref_1","first-page":"187","article-title":"Real-time INS\/GPS Integration with Optimal On-line Smoothing for Mobile Mapping Systems Utilizing a Low Cost MEMS IMU","volume":"45","author":"Duong","year":"2013","journal-title":"J. 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