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2020•Unpublished venueRequires access

Low cost manner of gaining car position utilizing odometry, MEMS gyroscope and magnetometer

Vojtech Šimák, Dušan Nemec, Jozef Hrbček, Emília Bubeníková

Open publisher page 1 citations

Abstract

This article deals with data fusion from vehicle’s odometer, gyroscope, and magnetometer. The accuracy of positioning using odometer is depending on driven distance instead of time increasing error in inertial navigation systems. The combination of odometer for translation and magnetometer compensated gyroscope for rotational information seems to be sufficient solution for short-term GNSS signal outages, like driving through tunnels.

About this research paper

What this paper is about

This article deals with data fusion from vehicle’s odometer, gyroscope, and magnetometer. The accuracy of positioning using odometer is depending on driven distance instead of time increasing error in inertial navigation systems. The combination of odometer for translation and magnetometer compensated gyroscope for rotational information seems to be sufficient solution for short-term GNSS signal outages, like driving through tunnels.

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OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Available abstract

This article deals with data fusion from vehicle’s odometer, gyroscope, and magnetometer. The accuracy of positioning using odometer is depending on driven distance instead of time increasing error in inertial navigation systems. The combination of odometer for translation and magnetometer compensated gyroscope for rotational information seems to be sufficient solution for short-term GNSS signal outages, like driving through tunnels.

Key concepts: Odometer, Gyroscope, Magnetometer, Odometry, Computer science, Inertial measurement unit, Vibrating structure gyroscope, Sensor fusion

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Low cost manner of gaining car position utilizing odometry, MEMS gyroscope and magnetometer — Research Paper | ScholarLens