gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»10.17285/0869-7035.2015.23.1.029-042gyroscopy-387Research ArticleСтатьиНавигационный алгоритм с использованием планов зданий и данных автономных датчиковДэвидсонП.

Дэвидсон Павел, доктор технических наук.

Киркко-ЯакколаМ.

Киркко-Яаккола Марти, доктор технических наук. 

КоллинЮ.

Колин Юсси, доктор технических наук. Кафедра компьютерных технологий Технологического университета (г. Тампере) Финляндия

ТакалаЯ.

Такала Ярмо, доктор технических наук. 

Университет ИТМО, кафедра информационно-навигационных систем (С.-Петербург)Russian FederationФинский институт геопространственных исследований. Национальная геодезическая служба (г. Киркконумми)FinlandКолин Юсси, доктор технических наук. Кафедра компьютерных технологий Технологического университета (г. Тампере) ФинляндияFinlandТехнологический университет, кафедра компьютерных технологий (г. Тампере)Finland2015181220252312942Copyright © Дэвидсон П., Киркко-Яаккола М., Коллин Ю., Такала Я., 20252025Дэвидсон П., Киркко-Яаккола М., Коллин Ю., Такала Я.Дэвидсон П., Киркко-Яаккола М., Коллин Ю., Такала Я.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/387

В статье представлен метод коррекции местоположения и курса навигационной системы с использованием поэтажных планов зданий. Алгоритм включает три этапа: (a) обработка данных автономных датчиков для определения местоположения и курса, (б) коррекция путем согласования с картой, и (в) оценка ошибок навигационной системы. Парциальный фильтр используется для комплексирования данных от навигационного счисления с планом здания. Фильтр Калмана оценивает ошибки счисления по курсу и местоположению. Алгоритм предназначен для навигационных систем объектов, работающих внутри зданий с известными планами этажей, и может быть адаптирован для реализации в навигационных системах реального времени с недорогими МЭМС-гироскопами и датчиками скорости в качестве инструментов счисления. Реальные данные, полученные при испытаниях объекта внутри зданий, продемонстрировали, что предлагаемый алгоритм способен исправлять значительные погрешности положения и курса.

 

This paper presents an approach to navigation system’s position and heading correction using building floor plans. The algorithm includes three steps: autonomous sensors data processing to obtain position and heading, map- matching correction, and navigation system errors estimation. A particle filter is used to incorporate the building plan information and a Kalman filter estimates the dead reckoning error states. This algorithm was designed for vehicle navigation systems operating inside buildings with known floor plans and can be adapted for implementation on real-time navigation systems using low-cost MEMS gyroscope and speed sensor as dead reckoning instruments. The real-world data collected from the vehicle indoor tests has shown that the proposed algorithm is able to correct significant errors in dead reckoning position and heading by applying the map constraints.

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