gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»gyroscopy-556Research ArticleСтатьиРобастный алгоритм инерциальной видеонавигации с использованием относительных оценок положенияДи КоратоФ.

Ди Корато Франческо, аспирант, старший лаборант. 

ИнночентиМ.

Инноченти Марио, профессор.        

ПоллиниЛ.

Поллини Лоренцо, доцент.         

Университет ПизыItaly2012100320262045070Copyright © Ди Корато Ф., Инноченти М., Поллини Л., 20262026Ди Корато Ф., Инноченти М., Поллини Л.Ди Корато Ф., Инноченти М., Поллини Л.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gyroscopy.ru/jour/article/view/556

Представлен слабосвязанный метод построения интегрированной навигационной системы, обеспечивающий улучшение качества оценок состояния в автономной инерциальной навигации за счет оценки относительного движения с использованием видеоданных. Применена новая методика оценки положения на основе минимизации энтропийно-подобной функции стоимости, по определению робастной к наличию шума и выбросов в визуальном изображении. Приводится экспериментальное подтверждение эффективности этой методики в сравнении с алгоритмом, используемым в настоящее время. В рамках стохастического клонирования для навигации используется непрямой фильтр Калмана. Робастная относительная оценка положения, полученная при использовании новой методики, передается в навигационный фильтр в качестве значения относительного положения. Представлены результаты моделирования, проведено их сравнение с результатами, полученными с помощью классического алгоритма прямого линейного преобразования на основе RANSAC.

The paper presents a loosely coupled approach for the improvement of state estimaion in autonomous inertial navigation, augmented via image_based relative motion estimation. The proposed approach uses a novel pose estimation technique based on the minimization of an entropy like cost function which is robust by nature to the presence of noise and outliers in the visual features. Experimental evidence of the performance of this approach is given and compared to a state of the art algorithm. An indirect Kalman filter is used for navigation in the framework of stochastic cloning. The robust relative pose estimation given by our novel technique is used to feed a relative position fix to the navigation filter. The simulation results are presented and compared with the results obtained via the classical RANSAC based direct linear transform approach.

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The authors declare that there are no conflicts of interest present.