An Integrated IMU, GNSS and Image Recognition Sensor for Pedestrian Navigation

C. Hide, T. Botterill, M. Andreotti

Abstract:	Foot mounted inertial sensors provide a promising method for accurate pedestrian positioning. By mounting sensors on a user’s foot, the accumulated position drift from dead reckoning can be reduced by applying zero velocity updates using a Kalman filter every time a user takes a step. However, such a system will still suffer from position drift unless occasional position updates are available. This paper describes a novel method for restricting such position drift using an image recognition algorithm. Firstly, a database of images and their locations is constructed over an area of interest. A user then navigates the area using foot-mounted inertial sensors and a video camera. As images are acquired, they are used to search the database of images using the Image Bag-of-Words algorithm. When new images are successfully matched with images in the database, the position from the database is used to update the inertial position using a Kalman filter. GNSS updates can also be used in the filter when available. The integrated inertial and vision system is demonstrated to provide better than 10m accuracy (typically 1-5m) over a period of 21 minutes. The system is relatively inexpensive, could run in real-time, does not require costly infrastructure, and could be deployed over larger areas.
Published in:	Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009) September 22 - 25, 2009 Savannah International Convention Center Savannah, GA
Pages:	527 - 537
Cite this article:	Hide, C., Botterill, T., Andreotti, M., "An Integrated IMU, GNSS and Image Recognition Sensor for Pedestrian Navigation," Proceedings of the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2009), Savannah, GA, September 2009, pp. 527-537.
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