Self-Alignment MEMS IMU Method Based on the Rotation Modulation Technique on a Swing Base

Xing, Haifeng; Chen, Zhiyong; Yang, Haotian; Wang, Chengbin; Lin, Zhihui; Guo, Mei

doi:10.3390/s18041178

Cited by 18 publications

(13 citation statements)

References 47 publications

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“…Kalman filtering (KF) has a good application for linear system filtering, but Kalman filter (KF) cannot filter nonlinear systems. For the nonlinear systems, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) were invented, and their principle is to convert nonlinear systems into linear systems for filtering [ 8 , 9 ]. The Extended Kalman Filter (EKF) approximates the nonlinear model to a linear model by Taylor expansion while the Unscented Kalman Filter (UKF) approximates the nonlinear model to a linear model through Unscented Transformation (UT); such methods can solve nonlinear model filtering, but they introduce a large amount of computation [ 10 – 12 ].…”

Section: Introductionmentioning

confidence: 99%

An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm

Wang

et al. 2020

Computational Intelligence and Neuroscience

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The shearer is one of the core equipment of the fully mechanized coal face. The fast and accurate positioning of the shearer is the prerequisite for its memory cutting, intelligent height adjustment, and intelligent speed adjustment. Inertial navigation technology has many advantages such as strong autonomy, good concealment, and high reliability. The accurate positioning of the shearer based on inertial navigation can not only determine its operating position but also measure the direction of movement. However, when inertial navigation is used to locate the shearer in motion, the cumulative errors will occur, resulting in inaccurate positioning of the shearer. The accuracy of the initial alignment is directly related to the working precision of the inertial navigation system. In order to improve the efficiency and accuracy of initial alignment, an improved initial alignment method is proposed in this paper, which uses a fruit fly-optimized Kalman filter algorithm for initial alignment. In order to improve the filtering performance, the fruit fly-optimized Kalman filter algorithm uses an improved fruit fly algorithm to realize the adaptive optimization of system noise variance. Finally, simulation and experiments verify the effectiveness of the fruit fly-optimized Kalman filter algorithm.

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Section: Introductionmentioning

confidence: 99%

An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…Nevertheless, local magnetic distortions may still corrupt the estimated heading, and the alignment accuracy can be further decreased by magnetic interference. Xing et al [10] proposed the rotation modulation technique at the initial stage, which is an option for initial alignment of the IMU; however, a rotating platform is needed, and the initial alignment time is too lengthy.…”

Section: Introductionmentioning

confidence: 99%

Novel Drift Reduction Methods in Foot-Mounted PDR System

Zhang

Yuan

2019

Sensors

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The zero-velocity update (ZUPT)-aided extended Kalman filter (EKF) is commonly used in the traditional inertial navigation system (INS)-based foot-mounted pedestrian dead reckoning (PDR) system, which can effectively suppress the error growth of the inertial-based pedestrian navigation systems. However, in the realistic test, the system still often suffers from drift, which is commonly caused by two reasons: failed detection of the stationary phase in the dynamic pedestrian gait and heading drift, which is a poorly observable variable of the ZUPT method. In this paper, firstly, in order to improve the initial heading alignment accuracy, a novel method to calibrate the PDR system’s initial absolute heading is proposed which is based on the geometric method. By using a calibration line rather than only using the heading of the starting point, the method can calibrate the initial heading of the PDR system more accurately. Secondly, for the problem of failed detection of the stationary phase in the dynamic pedestrian gait, a novel stationary phase detection method is proposed, which is based on foot motion periodicity rather than the threshold comparison principle in the traditional method. In an experiment, we found that the zero-speed state points always occur around the minimum value of the stationary detector in each gait cycle. By taking the minimum value in each gait cycle as the zero-speed state point, it can effectively reduce the failed detection of the zero-speed interval. At last, in order to reduce the heading drifts during walking over time, a new motion constraint method is exploited based on the range constraint principle. During pedestrian walking, the distance between the foot position estimates of the current moment and the previous stationary period is within the maximum stride length. Once the distance is greater than the maximum stride length, the constraint method is used to confine the current estimated foot position to the sphere of the maximum stride length relative to the previous stationary foot position. Finally, the effectiveness of all proposed methods is verified by the experiments.

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“…However, for the low-cost MEMS-IMU, the measurement noise is much larger than the Earth’s rotation rate in the IFBA method, which causes the practicability of the IFBA method in MEMS-IMU error calibration to become very weak. Consider this: Haifeng Xing et al [22] introduced RMT (rotary modulation technology) into the IFBA method, and applied a STF (strong tracking filter) algorithm to suppress the random noise of MEMS-IMU, which improved the calibration accuracy of MEMS-IMU greatly. However, that method still needs the support of a rotating platform, and is not suitable for the onsite calibration.…”

Section: Introductionmentioning

confidence: 99%

An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields

Niu

Feng

et al. 2019

Sensors

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Light detection and ranging (LiDAR) is one of the popular technologies to acquire critical information for building information modelling. To allow an automatic acquirement of building information, the first and most important step of LiDAR technology is to accurately determine the important gesture information that micro electromechanical (MEMS) based inertial measurement unit (IMU) sensors can provide from the moving robot. However, during the practical building mapping, serious errors may happen due to the inappropriate installation of a MEMS-IMU. Through this study, we analyzed the different systematic errors, such as biases, scale errors, and axial installation deviation, that happened during the building mapping, based on a robot equipped with MEMS-IMU. Based on this, an error calibration model was developed. The problems of the deviation between the calibrated and horizontal planes were solved by a new sampling method. For this method, the calibrated plane was rotated twice; the gravity acceleration of the six sides of the MEMS-IMU was also calibrated by the practical values, and the whole calibration process was completed after solving developed model based on the least-squares method. Finally, the building mapping was then calibrated based on the error calibration model, and also the Gmapping algorithm. It was indicated from the experiments that the proposed model is useful for the error calibration, which can increase the prediction accuracy of yaw by 1–2° based on MEMS-IMU; the mapping results are more accurate when compared to the previous methods. The research outcomes can provide a practical basis for the construction of the building information modelling model.

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Self-Alignment MEMS IMU Method Based on the Rotation Modulation Technique on a Swing Base

Cited by 18 publications

References 47 publications

An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm

An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm

Novel Drift Reduction Methods in Foot-Mounted PDR System

An Onsite Calibration Method for MEMS-IMU in Building Mapping Fields

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