Hardware support for research of the sensor fusion of inertial sensors

被引:1
作者
Andel, Jan [1 ]
Simak, Vojtech [1 ]
机构
[1] Univ Zilina, Dept Control & Informat Syst, Zilina, Slovakia
来源
2022 INTERNATIONAL CONFERENCE ON APPLIED ELECTRONICS, AE | 2022年
关键词
MSE; IMU; data fusion; sensors; error;
D O I
10.1109/AE54730.2022.9920062
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The aim of this paper was to propose a design of a module that has several inertial sensors of the same type in order to test various approaches of homogeneous sensor fusion. According to the statistics the mean of readings from the same-type sensors should have higher precision than a single sensor.However, this statement is not always correct for real sensors, as identical sensors may not have the same error characteristics. Sensor manufacturers state the typical sensor RMS (root mean square) error, the actual sensor RMS error can differ significantly from piece to piece. When averaging a sensor output from the same manufacturer, we can under certain conditions, obtain a worse value than the output error of the best sensor. This error can be eliminated by fusing the sensors using weighing. To verify this statement, we decided to assemble with as many identical sensors as possible. The IMU (inertial measurement unit) sensor, which measures acceleration, angular acceleration, and magnetic field in three axes, was chosen as the sensor for the variety of measurements. Thanks to this, we can compare up to 9 different outputs at the same time. In the end, we designed a module that has 16 IMUs. As the number of sensors increases, the resulting error decreases on average. However, weighting based on calibration errors did not prove to be the optimal solution because the sensors contain not only stochastic but also systematic errors. The module designed by us will be used mainly for further scientific research in the field of IMU sensor fusion in order to reduce the error.
引用
收藏
页码:15 / 18
页数:4
相关论文
共 50 条
[41]   Semi-Automatic Sensor-to-Body Calibration of Inertial Sensors on Lower Limb Using Gait Recording [J].
Nazarahari, Milad ;
Rouhani, Hossein .
IEEE SENSORS JOURNAL, 2019, 19 (24) :12465-12474
[42]   Using collective decision system support to manage error in wireless sensor fusion [J].
Urken, AB .
2005 7TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), VOLS 1 AND 2, 2005, :1605-1612
[43]   A MULTI-AGENT ARCHITECTURE TO SUPPORT ACTIVE FUSION IN A VISUAL SENSOR NETWORK [J].
Castanedo, Federico ;
Garcia, Jesus ;
Patricio, Miguel A. ;
Molina, Jose M. .
2008 SECOND ACM/IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED SMART CAMERAS, 2008, :374-381
[44]   When Video meets Inertial Sensors: Zero-shot Domain Adaptation for Finger Motion Analytics with Inertial Sensors [J].
Liu, Yilin ;
Zhang, Shijia ;
Gowda, Mahanth .
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET-OF-THINGS DESIGN AND IMPLEMENTATION, IOTDI 2021, 2021, :182-194
[45]   Research of Detecting Runway Debris Based on the Multi-sensors Data Fusion [J].
Guo Xiao-jing ;
Yang Xue-you ;
Yu Zhi-jing .
2011 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, 2011, :1076-+
[46]   Research on data fusion of multi-sensors based on fuzzy preference relations [J].
Hao, Huijuan ;
Wang, Maoli ;
Tang, Yongwei ;
Li, Qingdang .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (Suppl 1) :337-346
[47]   On the use of a full stack hardware/software infrastructure for sensor data fusion and fault prediction in industry 4.0 [J].
De Vita, Fabrizio ;
Bruneo, Dario ;
Das, Sajal K. .
PATTERN RECOGNITION LETTERS, 2020, 138 :30-37
[48]   Research on data fusion of multi-sensors based on fuzzy preference relations [J].
Huijuan Hao ;
Maoli Wang ;
Yongwei Tang ;
Qingdang Li .
Neural Computing and Applications, 2019, 31 :337-346
[49]   Research on real time 6DOF robot localization based on visual and inertial fusion [J].
Zheng, Xinfang ;
Guo, Yongxiang .
PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY (ICMMCT 2017), 2017, 126 :1392-1402
[50]   Research on real time 6DOF robot localization based on visual and inertial fusion [J].
Zheng, Xinfang ;
Guo, Yongxiang .
PROCEEDINGS OF THE 2017 5TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY (ICMMCT 2017), 2017, 126 :1431-1441