Multisensor Fusion Using Fuzzy Inference System for a Visual-IMU-Wheel Odometry

Zhu, Jie; Tang, Yujie; Shao, Xin; Xie, Yangmin

doi:10.1109/tim.2021.3051999

Cited by 28 publications

(11 citation statements)

References 31 publications

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“…The commonly used information sources can be categorized into real observations and virtual observations. The former is provided by physical sensors, such as odometers, steering encoders, barometers, and cameras [8][9][10][11], and the latter is offered by motion aided constraints (MACs) [12,13] or various neural networks [14,15].…”

Section: Introductionmentioning

confidence: 99%

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Mu¹,

Long²

2022

Meas. Sci. Technol.

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Due to low cost and complementary performance advantages, GNSS/SINS integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints (MACs) selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame (VBF) when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm between the origins of the odometer and IMU, and the non-zero lateral velocity generated by turning maneuvers are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 minutes. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

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Section: Introductionmentioning

confidence: 99%

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Mu¹,

Long²

2022

Meas. Sci. Technol.

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“…Variants of the EKF, such as the UKF [ 260 ], Multi-State Constraint Kalman Filter (MSCKF) [ 261 , 262 ]—which incorporates poses of past frames to marginalize features from the state space. Iterated EKF [ 263 ], Cubature Kalman Filter (CKF) [ 264 ], fuzzy logic KF [ 265 ], covariance intersection KF [ 266 ] and invariant EKF based on the Lie group [ 267 ], have been proposed in the literature.…”

Section: Sensor Fusionmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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“…Multi-sensor fusion technology can fuse the load sensor signal with other sensor signals, make regression prediction of the real weight of the material in the drying process, and further detect the moisture content of the material. Regression prediction based on multi-sensor fusion technology has been widely used in other industries, such as the remaining life prediction of aviation engines (Li et al, 2022b), tool wear prediction (Meng et al, 2021), air pollution level prediction (Ari and Alagoz, 2022), and wheel odometry prediction (Zhu et al, 2021). Kirsanov et al (2021) used PLSR to relate the sensor signals to the values of different water quality parameters, which enabled the accurate detection of various water quality parameters.…”

Section: Introductionmentioning

confidence: 99%

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

Yang,

Zheng,

Xiao

et al. 2024

Front. Plant Sci.

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To monitor the moisture content of agricultural products in the drying process in real time, this study applied a model combining multi-sensor fusion and convolutional neural network (CNN) to moisture content online detection. This study built a multi-sensor data acquisition platform and established a CNN prediction model with the raw monitoring data of load sensor, air velocity sensor, temperature sensor, and the tray position as input and the weight of the material as output. The model’s predictive performance was compared with that of the linear partial least squares regression (PLSR) and nonlinear support vector machine (SVM) models. A moisture content online detection system was established based on this model. Results of the model performance comparison showed that the CNN prediction model had the optimal prediction effect, with the determination coefficient (R2) and root mean square error (RMSE) of 0.9989 and 6.9, respectively, which were significantly better than those of the other two models. Results of validation experiments showed that the detection system met the requirements of moisture content online detection in the drying process of agricultural products. The R2 and RMSE were 0.9901 and 1.47, respectively, indicating the good performance of the model combining multi-sensor fusion and CNN in moisture content online detection for agricultural products in the drying process. The moisture content online detection system established in this study is of great significance for researching new drying processes and realizing the intelligent development of drying equipment. It also provides a reference for online detection of other indexes in the drying process of agricultural products.

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Multisensor Fusion Using Fuzzy Inference System for a Visual-IMU-Wheel Odometry

Cited by 28 publications

References 31 publications

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Moisture content online detection system based on multi-sensor fusion and convolutional neural network

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