Feature Error Model for Integrity of Pattern-based Visual Positioning

Chen Zhu, Christian Steinmetz, Boubeker Belabbas, and Michael Meurer

Abstract: Camera-based visual navigation techniques can provide high precision infrastructure-less localization solutions using visual patterns, and play an important role in the environments where satellite navigation has significantly degraded performance in availability, accuracy, and integrity. However, the integrity monitoring of visual navigation methods is an essential but hardly-solved topic, since modelling the geometric error for cameras is rather challenging. This work proposes a highprecision geometric error model of detected feature corners for chessboard-like patterns. The model is named as Chessboard Corner Geometric Error Model (CCGEM). By applying the model to images containing chessboard-like patterns, the extracted corner location accuracy can be predicted in different lighting conditions. The coefficients in the model can be adapted to each distinct camera-lens system through a calibration process. The proposed method first models the intensity distribution in the local neighboring area of the extracted corner by taking the raw image as measurement input. Then, the geometric error of the feature location is modelled as a function of the distribution parameters. We show that the model fits the measurement error well in both simulated and real images. The proposed CCGEM also provides a conservative fitting model with risk probability information, which can be applied in the integrity monitoring of vision-based positioning.
Published in: Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019)
September 16 - 20, 2019
Hyatt Regency Miami
Miami, Florida
Pages: 2254 - 2268
Cite this article: Zhu, Chen, Steinmetz, Christian, Belabbas, Boubeker, Meurer, Michael, "Feature Error Model for Integrity of Pattern-based Visual Positioning," Proceedings of the 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019), Miami, Florida, September 2019, pp. 2254-2268.
https://doi.org/10.33012/2019.16956
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