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- Title
A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment.
- Authors
Chenguang Ouyang; Suxing Hu; Fengqi Long; Shuai Shi; Zhichao Yu; Kaichun Zhao; Zheng You; Junyin Pi; Bowen Xing
- Abstract
Accurate positioning is one of the essential requirements for numerous applications of remote sensing data, especially in the event of a noisy or unreliable satellite signal. Toward this end, we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera, an altimeter, a compass, and an open-source Vector Map (VMAP). The algorithm combines the matching and particle filter methods. Shape vector and correlation between two building contour vectors are defined, and a coarse-to-fine building vector matching (CFBVM) method is proposed in the matching stage, for which the original matching results are described by the Gaussian mixture model (GMM). Subsequently, an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles, and a credibility indicator is designed to avoid location mistakes in the particle filter stage. An experimental evaluation of the approach based on flight data is provided. On a flight at a height of 0.2 km over a flight distance of 2 km, the aircraft is geo-localized in a reference map of 11,025 km2 using 0.09 km² aerial images without any prior information. The absolute localization error is less than 10 m.
- Subjects
VECTOR data; AIRCRAFT industry; GLOBAL Positioning System; GAUSSIAN mixture models; COMPUTER algorithms
- Publication
Defence Technology, 2024, Vol 34, p1
- ISSN
2096-3459
- Publication type
Article
- DOI
10.1016/j.dt.2023.07.006