gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya / Gyroscopy and Navigation0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»10.17285/0869-7035.2016.24.4.098-111gyroscopy-379Research ArticleСтатьиОценка дальности на основе монокулярного зрения и проприоцептивного движенияMonocular vision based range estimation supported by proprioceptive motionДавидсонП.Davidson P.P.

Давидсон Павел, доктор наук, научный сотрудник. 

РауниоЮ.-П.RaunioJ-P.

Раунио Юкка-Пекка, доктор наук, научный сотрудник. 

ПишеР.PichéR.

Пише Роберт, профессор. 

Технологический университет г. Тампере.ФинляндияTampere University of TechnologyFinland20160312202524498111Copyright © Давидсон П., Раунио Ю., Пише Р., 20252025Давидсон П., Раунио Ю., Пише Р.Davidson P. P., Raunio J., Piché R.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/379

В статье описывается метод комплексирования измерений монокулярной камеры, движения камеры, а также измерений одометров и инерциальных датчиков скорости. Движение камеры между последовательными изображениями создает базовую линию для расчета дальности путем триангуляции. Рекурсивный алгоритм оценивания основывается на расширенном фильтре Калмана. На точность оценивания глубины сильно влияют взаимная геометрия наблюдателя и ориентира, точность измерения параметров движения наблюдателя и линия визирования на ориентир. С помощью моделирования исследуется, как влияют на точность оценивания погрешности измерения линейной и угловой скорости, шум камеры и траектория наблюдателя. Из результатов этих исследований можно вывести требования к измерительной аппаратуре и сценариям наблюдения. При благоприятных условиях погрешность оценки дальности не превышает 2% от расстояния до ориентира.

This paper describes an approach for fusion of monocular vision measurements, camera motion, odometer and inertial rate sensor measurements. The motion of the camera between successive images generates a baseline for range computations by triangulation. The recursive estimation algorithm is based on extended Kalman filtering. The depth estimation accuracy is strongly affected by the mutual observer and feature point geometry, measurement accuracy of observer motion parameters and line of sight to a feature point. The simulation study investigates how the estimation accuracy is affected by the following parameters: linear and angular velocity measurement errors, camera noise, and observer path. These results impose requirements to the instrumentation and observation scenarios. It was found that under favorable conditions the error in distance estimation does not exceed 2% of the distance to a feature point.

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