Improved position estimation by fusing multiple inaccurate inertial measurement unit sensors

2016 ◽  
Author(s):  
Yuhao Zhang ◽  
Chenghao Lyu ◽  
Haotian Xu ◽  
Yijing Xia ◽  
Fei Feng ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Position Estimation ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals An Implementation of Error Minimization Position Estimate in Wireless Inertial Measurement Unit using Modification ZUPT

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Adytia Darmawan ◽  
Sanggar Dewanto ◽  
Dadet Pramadihanto
Keyword(s):  
Inertial Measurement Unit ◽  
Robot Navigation ◽  
Angular Rate ◽  
Position Estimation ◽  
Experimental Result ◽  
Measurement Unit ◽  
Legged Robot ◽  
Positioning Systems ◽  
Inertial Measurement ◽  
Position Estimate

Position estimation using WIMU (Wireless Inertial Measurement Unit) is one of emerging technology in the field of indoor positioning systems. WIMU can detect movement and does not depend on GPS signals. The position is then estimated using a modified ZUPT (Zero Velocity Update) method that was using Filter Magnitude Acceleration (FMA), Variance Magnitude Acceleration (VMA) and Angular Rate (AR) estimation. Performance of this method was justified on a six-legged robot navigation system. Experimental result shows that the combination of VMA-AR gives the best position estimation.

scholarly journals Positioning Using Terrestrial Multipath Signals and Inertial Sensors

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Christian Gentner ◽  
Robert Pöhlmann ◽  
Markus Ulmschneider ◽  
Thomas Jost ◽  
Siwei Zhang
Keyword(s):  
Particle Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Multipath Propagation ◽  
Position Estimation ◽  
Measurement Unit ◽  
Angle Of Arrival ◽  
Inertial Measurement ◽  
Multipath Signals ◽  
Mobile Receiver

This paper extends an algorithm that exploits multipath propagation for position estimation of mobile receivers named Channel-SLAM. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) and estimates the positions of the VTs simultaneously with the mobile receiver positions. For Channel-SLAM it is essential to obtain angle of arrival (AoA) measurements for each MPC in order to estimate the VT positions. In this paper, we propose a novel Channel-SLAM implementation based on particle filtering which fuses heading information of an inertial measurement unit (IMU) to omit AoA measurements and to improve the position accuracy. Interpreting all MPCs as signals originated from VTs, Channel-SLAM enables positioning also in non-line-of-sight situations. Furthermore, we propose a method to dynamically adapt the number of particles which significantly reduces the computational complexity. A posterior Cramér-Rao lower bound for Channel-SLAM is derived which incorporates the heading information of the inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a single fixed transmitter and a moving pedestrian carrying the receiver and the IMU. The evaluations show that accurate position estimation is possible without the knowledge of the physical transmitter position by exploiting MPCs and the heading information of an IMU.

Pipeline Inspection Gauge Position Estimation Using Inertial Measurement Unit, Odometer, and a Set of Reference Stations

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Md Sheruzzaman Chowdhury ◽  
Mamoun F. Abdel-Hafez
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Position Estimation ◽  
Measurement Unit ◽  
State Estimate ◽  
Inertial Measurement ◽  
Pipeline Inspection ◽  
Global Positioning ◽  
To Receive

This paper presents a low-cost methodology to estimate the position of a pipeline inspection gauge (PIG). The environment in which the PIG navigates is inside the thick walls of a metallic pipeline, where it is not possible to receive a global positioning system (GPS) signal. As a consequence, it is necessary to use other means of navigation. A technique is presented in the paper that uses an inertial measurement unit (IMU), a speedometer, and a set of reference stations. A Kalman filter is used to fuse the measurements from the IMU, the speedometer, and the reference stations. The reference stations, with known GPS coordinates, are installed for every set interval to correct the PIG’s state estimate from the errors that accumulate due to the integration of the IMU measurements. The paper presents three scenarios. These scenarios differ in the way the update step of the Kalman filter is performed. Experimental results are presented along with a 100-run Monte Carlo test to verify the estimator’s consistency.

scholarly journals Pedestrian navigation system based on the inertial measurement unit sensor for outdoor and indoor environments

2020 ◽  
Vol 9 (1) ◽  
pp. 7-13
Author(s):  
Marcin Uradzinski ◽  
Hang Guo
Keyword(s):  
Inertial Measurement Unit ◽  
Angular Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Position Estimation ◽  
Outdoor Environment ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Inertial Measurement ◽  
Step Detection

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.

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