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To improve the performance of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method involves the use of a low-cost magnetic, angular rate, and gravity sensor and an odometer. This study addresses the problems of large and abrupt changes in acceleration by moving vehicles and t...
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Citations
... Although the magnetometer itself does not produce cumulative errors, it needs to be calibrated before use and is easily disturbed by changes in the external magnetic field. Li et al. [18] proposed a 12-parameter fitting method, which can better compensate the soft magnetic field interference of the surrounding environment. e study [19] used a support vector regression machine to realize a software-based antimagnetic interference method for MWD. e simulation results show that the predicted azimuthal error is less than 1°, which proves the feasibility of magnetometer for applications in subterranean coal mine environments. ...
The key technology to realize intelligent unmanned coal mining is the strapdown inertial navigation system (SINS); however, the gradual increase of cumulative error during the working process needs to be solved. On the basis of an SINS/odometer (OD)-integrated navigation system, this paper adds magnetometer (MAG)-aided positioning and proposes an SINS/OD/MAG-integrated shearer navigation system. The velocity observation equation is obtained from the speed constraints during shearer movement, and the yaw angle observation equation is obtained from the magnetometer output. The position information of the SINS output is calibrated using these two observations. In order to improve the fault tolerance of the integrated navigation system, an adaptive federated Kalman filter is established to complete the data fusion of the SINS. Experimental results show that the positioning accuracy of the SINS/OD/MAG-integrated navigation system is 75.64% and 74.01% higher in the east and north directions, respectively, than the SINS/OD-integrated navigation system.
... Extensive research has been performed for using the Kalman filter effectively to combine two measurements and obtaining accurate position estimation. Compared to the traditional Euler angle-based model and quaternions, the extended Kalman filter based on the direction cosine matrix (DCM) method avoids singularity and nonlinearity and exerts a better effect [18]. The technique of transforming a vector from one coordinate reference frame to another is yield by a DCM. ...
... So that the state transition model for the second filter of Stage 1 will be expressed through rotation matrices. Therefore, the DCM Computation block is introduced in the proposed approach to evaluate the transformation from the b-frame to the l-frame through (18)(19)(20) found in [22,23]: ...
Despite the fact that accelerometers and gyroscopes are used in inertial navigation systems (INS) to provide navigation information without the aid of external references, accumulated systematic errors are shown in sensor readings on long-term usage. In this work, a new approach is proposed to overcome this problem, by using extended Kalman filter (EKF)—linear Kalman filter (LKF), in a cascaded form, to couple the GPS with INS. GPS raw data are fused with noisy Euler angles coming from the inertial measurement unit (IMU) readings, in order to produce more consistent and accurate real-time navigation information. The proposed algorithm is designed to run through three software threads simultaneously. The multi-thread processing provides better use of hardware resources and applies more efficient INS/GPS loosely coupled integration scheme compared to the conventional method. Two datasets are used to verify the efficacy of the proposed approach against the existing GPS/INS coupling techniques. The first set is synthetic data generated by MATLAB that represents a static vehicle at known coordinates. The second one is a real road test data collected in Ontario, Canada. Accordingly, the root mean square error (RMSE) values for the proposed approach in ENU directions have reached 0.022, 0.034 and 0.010 m, respectively, for a static vehicle, as well as 0.493, 0.453 and 0.110 m, respectively, for a movable vehicle—which is notably competitive with other recent related work.
... Extensive research has been performed for effectively using the KF to combine two measurements and obtaining accurate position estimation. Compared to the traditional Euler angle-based model and quaternions, the extended Kalman filter based on the direction cosine matrix (DCM) method avoids singularity and nonlinearity and exerts a better effect [13]. ...
This paper presents a novel approach for dead reckoning. The described localization system consists of an inertial navigation system (INS), a magnetic, angular rate, gravity sensor (MARG sensor) and an odometry an odometry model. In contrast to conventional odometry models, a kinematic two-track model is implemented. The odometry model uses wheel-individual steering angles. It can therefore be applied for vehicles with all-wheel steering and steering geometries that allow opposite steering angle directions at one axle. An error-state Kalman filter is used to merge the individual submodels. While conventional odometry based localization systems only consider the vehicle longitudinal and lateral speed and position change, the proposed localization system is also able to correctly represent movements along the vehicles vertical axis. Furthermore, the algorithm uses the vehicle acceleration calculated from the odometry model to increase the robustness of the orientation estimation. The aim of the localization method is a high positioning accuracy in the low speed range, e.g. during precise maneuvering or parking. For this reason, the accuracy of the localization system is demonstrated by driving tests on a parking lot. The all-electric vehicle platform flexCAR is used as a test vehicle. Through its symmetrical design, this vehicle is able to realize wheel steering angles of up to 30° on the front and rear axles. Due to its maneuverability it is particularly suitable for the investigation of parking and (maneuvering).KeywordsVehicle-Self-LocalizationDead ReckoningMARGAHRSINSOdometryError-State Kalman Filter (ESKF)
A method for performing 3D motion tracking of the shoulder joint with respect to the thorax, using MARG sensors and a data fusion algorithm, is proposed. Two tests were done: (1) qualitative and quantitative analysis of the response of the sensors, static position and during motion, with and without the proposed data fusion algorithm; (2) motion tracking of the shoulder joint with the upper arm, the thorax, and the shoulder joint respect to the thorax. Qualitative analysis of experimental results showed that despite slight variations regarding the evaluated motion, these variations did not have repercussions on the estimated orientation. Quantitative analysis showed that the estimated orientation did not exhibit significant variations, in five minutes, such as drift errors (about 0.1° in static position and less than 1.8° during motion), variations due to noise or magnetic disturbances (RMSE less than 0.04° static position and less than 1° during motion); no singularity problems were reported.
The main contributions of this research are a multisensor data fusion algorithm, which combines the complementary properties of gyroscopes, accelerometers, and magnetometers in order to estimate the 3D orientation of two body segments separately and with respect to another body segment considering the spatial relationship between them; and a method for performing 3D motion tracking of two body segments, based on the estimation of their orientation, including motion compensation. The proposed method is applicable to monitoring devices based on IMU/MARG sensors; the performance was evaluated using a customized motion analysis system.
