Compensated Heading Angles for Outdoor Mobile Robots in Magnetically Disturbed Environment

Lee, Jehong; Lim, Jeonggeun; Lee, Jong-Ho

doi:10.1109/tie.2017.2726958

Cited by 32 publications

(15 citation statements)

References 27 publications

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“… The angular velocity: it can be used to detect rotational acceleration (especially the centripetal acceleration) [ 28 ], but it is not always reliable since the turning radius is usually unknown. The magnetic dip (i.e., the angle between

and the horizontal plane): it can be used to detect

[ 21 , 27 ], but with the requisite that the pitch and roll angles are accurately known. …”

Section: Previous Workmentioning

confidence: 99%

“…The magnetic dip (i.e., the angle between

and the horizontal plane): it can be used to detect

[ 21 , 27 ], but with the requisite that the pitch and roll angles are accurately known.…”

Section: Previous Workmentioning

confidence: 99%

“…For instance, the motional acceleration of carrier (also known as external acceleration) will be vectoral added to gravity, and hence the calculation of pitch and roll angles will be affected [ 1 , 2 , 3 ]. On the other hand, external magnetic interference can lead to significant heading error [ 21 ], since geomagnetic field intensity is very low. Such external disturbances cannot be thoroughly solved by the above-mentioned error calibration and data fusion methods, since most of these methods are mainly based on time-invariant models.…”

Section: Introductionmentioning

confidence: 99%

“…They are also called switching filters, and can be further divided into ‘hard-switching’ and ‘soft-switching’ ones. The former directly excludes the vector field sensor from the filter once it is affected by external disturbance [ 3 , 21 , 22 ], while the latter gradually reduces the weight of the affected sensor for data fusion when the disturbance increases [ 1 , 9 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems

Wang

2020

Micromachines

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The attitude and heading reference system (AHRS), which consists of tri-axial magnetometer, accelerometer, and gyroscope, has been widely adopted for three-dimensional attitude determination in recent years. It provides an economical means of passive navigation that only relies on gravity and geomagnetic fields. However, despite the advantages of small size, low cost, and low power, the magnetometer and accelerometer are susceptible to external disturbances, such as the magnetic interference from nearby ferromagnetic objects and current-carrying conductors, as well as the motional acceleration of the carrier. To eliminate such disturbances, a vector-based parallel structure is introduced for the attitude filter design, which can avoid the mutual interference between gravity and geomagnetic vectors. Meanwhile, an approach to estimate and compensate the external disturbances in real time for magnetometer and accelerometer is also presented. Compared with existing designs, the proposed filter architecture and external disturbance rejection algorithm can feasibly and effectively cooperate with mainstream data fusion techniques, including complementary filter and Kalman filter. According to experiment results, in the case that large and persistent external disturbances exist, the proposed method can improve the accuracy and robustness of attitude estimation, and it outperforms the existing methods such as switching filter and adaptive filter. Furthermore, through the experiments, the critical role of fading factor in handling the external disturbance is revealed.

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and the horizontal plane): it can be used to detect

[ 21 , 27 ], but with the requisite that the pitch and roll angles are accurately known. …”

Section: Previous Workmentioning

confidence: 99%

“…The magnetic dip (i.e., the angle between

and the horizontal plane): it can be used to detect

[ 21 , 27 ], but with the requisite that the pitch and roll angles are accurately known.…”

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems

Wang

2020

Micromachines

View full text Add to dashboard Cite

show abstract

“…The algorithm could accurately identify abnormal data and realize standard statistical test of filter residual, but the influence of the filter gain matrix on the algorithm was neglected. Lee [6] introduced an algorithm detecting and eliminating magnetic disturbance in the geomagnetic field. The algorithm was implemented in a mini type microprocessor and sensor module with relatively low costs by combining the extended Kalman filter, but it is unsuitable for robot localization in the area without magnetic disturbance.…”

Section: State Of the Artmentioning

confidence: 99%

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Cheng¹,

Liu²,

Meng³

et al. 2018

JESTR

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Accumulative errors can be retained all the time when classical Kalman filtering is adopted for odometer-based dead reckoning, thereby affecting self-localization accuracy of the robot. A mobile robot self-localization method based on limited memory Kalman filtering (LMKF) with exponential fading factor was proposed to reduce accumulative errors of the odometer and improve localization accuracy of the mobile robot. The self-localization system of mobile robot was built. A mathematical model was established based on LMKF with exponential fading factor. A dead reckoning method fusing multi-sensor information was proposed. The model accuracy was verified through simulation and test. Results indicate that the LMKF method with exponential fading factor positively affects the tracking of high-speed maneuvering dynamic targets, and its localization error is reduced by 42.5% compared with the odometer-based dead reckoning. The tracking accuracy of the mobile robot is stable at 0.5 m. This study can provide references for mobile robot selflocalization using multi-sensor.

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Heading angle estimation using rotating magnetometer for mobile robots under environmental magnetic disturbances

Shi

Lai

et al. 2020

Intel Serv Robotics

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Compensated Heading Angles for Outdoor Mobile Robots in Magnetically Disturbed Environment

Cited by 32 publications

References 27 publications

External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems

External Disturbances Rejection for Vector Field Sensors in Attitude and Heading Reference Systems

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Heading angle estimation using rotating magnetometer for mobile robots under environmental magnetic disturbances

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