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- Inertial navigation system (4)
- Embedded systems (3)
- Inertial measurement unit (3)
- Person tracking (3)
- Sensor calibration and validation (3)
- 3D sensor (2)
- Wireless sensor network (WSN) (2)
- 3D calibration method (1)
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Inertial Navigation Systems with three 3D sensors are used to localize moving persons. The accuracy of the localization depends on the quality of the sensor data of the multi-sensor system. In order to improve the accuracy, a self-calibration process based on the automatic 3D calibration was developed. Based on the calibration procedure of the accelerometer (ACC) and the magnetic field sensor (MAG), the additional integration of the gyroscope (GYRO) leads to a reduction of the indoor positioning error. This improves both the approximation for the accelerometer and the magnetic field sensor so that the standard deviation of a single sensor is minimized. A new calibration procedure of the gyroscope and the accuracy improvement of the localization of a moving person are presented.
For the accuracy of inertial navigation systems for indoor localization it is important to get high quality sensor data of the multi-sensor system. This can be realized using high quality sensors or the developed 3D-self-calibration-method for low cost sensors. Based on the calibration procedure of the accelerometer (ACC) and the magnetic field sensor (MAG), the additional integration of the gyroscope (GYRO) leads to a reduction of the indoor positioning error. This improves both the approximation for the accelerometer, the magnetic field sensor and the gyroscope so that the standard deviation of a single sensor is minimized. There are errors in the whole system. To determine these error sources it is important to define the measurement uncertainty. In this paper it is presented that the measurement uncertainty can be reduced by the application of the developed 3D-self-calibration method.
The calibration of the integrated sensors in a multisensor system has gained in interest over the last years. In this paper we introduce an enhanced calibration process, which is based on the preceding study described in. The enhancement consists of the integration of a gyroscope. So far only the accelerometer and the magnetic field sensor were taken into account for the calibration process. Due to this improvement we reach a better approximation of the accelerometer and the magnetic field sensor. Additionally, we minimize the standard
deviation of the single sensors and improve the accuracy of the positioning of a moving person.
A multi-sensor system for 3D localization was developed and named BodyGuard. It combines body movement sensing and a guard system for the tracking and recording of the status of persons. BodyGuard was designed to monitor and transmit the movement of a person radio-based and to transform that data into a spatial coordinate. This paper describes how the BodyGuard system works, what components the system consists of, how the individual sensor data is converted into 3D motion data, with which algorithms the individual sensors are processed, how individual errors are compensated and how the sensor data are fused into a 3D Model.
Sensor based person tracking is a challenging
topic. The main objective is positioning in areas without
GPS connection, i.e. indoors. A research project is carried
out at BAM, Federal Institute for Materials Research and
Testing, to develop and to validate a multi-sensor system for
3D localization. It combines body motion sensing and a
guard system for the tracking and recording of the status of
persons. The so named BodyGuard system was designed for
sensor-based monitoring and radio-based transmission of
the movement of a person. Algorithms were developed to
transform the sensor data into a spatial coordinate. This
paper describes how the BodyGuard system operates, which
main components were used in the system, how the
individual sensor data are converted into 3D motion data,
with which algorithms the individual sensors are processed,
how individual errors are compensated and how the sensor
data are merged into a 3D Model. Final objective of the
BodyGuard system is to determine the exact location of a
person in a building, e.g. during fire-fighting operations.