Vision-based Navigation using Gaussian Mixture Model of Terrain Features

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A new method with an artificial intelligence technique for vision-based navigation is proposed in this paper. In order to overcome the vulnerabilities of GPS/INS such as jamming and spoofing, vision-based navigation has been widely researched. However, most of the methods for vision-based navigation did not consider the difference between the scene and the database. Therefore, we adopted the semantic segmentation to extract the time-invariant features in the aerial imagery and approached the matching problem as comparing two Gaussian mixture models. Based on L2 distance between two Gaussian mixture models, the particle filter estimates the region most similar to the aerial imagery. For demonstrating the performance, the simulation is implemented in the terrain with dense and sparse features, respectively. Furthermore, the particle filter estimated the true trajectory within the horizontal error of 10m a regardless of the terrain.
Publisher
APISAT
Issue Date
2019-12-06
Language
English
Citation

ASIA PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY 2019

DOI
10.2514/6.2020-1344
URI
http://hdl.handle.net/10203/272472
Appears in Collection
AE-Conference Papers(학술회의논문)
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