Particle Filter Approach to Vision-Based Navigation with Aerial Image Segmentation

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This study proposes a novel approach for a vision-based navigation problem using semantically segmented aerial images generated by a convolutional neural network. Vision-based navigation provides a position solution by matching an aerial image to a georeferenced database, and it has been increasingly studied for global navigation satellite system-denied environments. Aerial images include a vast amount of information that infers the position where they are located. However, it also includes features that disturb the estimation accuracy. The progress of convolutional neural network may provide a promising solution for extracting only helpful features for this purpose. Therefore, segmented images are modeled as a Gaussian mixture model, and the L2 distance for a quantitative discrepancy between two images is established. This allows us to compare the two images quickly with improved accuracy. In addition, a framework of a particle filter is applied to estimate the position using an inertial navigation system. It employs the L2 distance as a measurement, and the particles tend to converge to the true position. Flight test experiments were conducted to verify that the proposed approach achieved distance error of less than 10 m.
Publisher
AMER INST AERONAUTICS ASTRONAUTICS
Issue Date
2021-12
Language
English
Article Type
Article
Citation

JOURNAL OF AEROSPACE INFORMATION SYSTEMS, v.18, no.12, pp.964 - 972

ISSN
2327-3097
DOI
10.2514/1.I010957
URI
http://hdl.handle.net/10203/296829
Appears in Collection
AE-Journal Papers(저널논문)
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