Application of data fusion algorithm based on kalman filter in mobile robot position measuring system

2004 ◽  
Author(s):  
Yizhong Lin ◽  
Yumei Huang ◽  
Enxiu Shi
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Mobile Robot ◽  
Measuring System ◽  
Fusion Algorithm ◽  
Robot Position

scholarly journals Robust Data Fusion of UAV Navigation Measurements with Application to the Landing System

2020 ◽  
Vol 12 (23) ◽  
pp. 3849
Author(s):  
Kirill Kolosov ◽  
Alexander Miller ◽  
Boris Miller
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Measurement Errors ◽  
High Sensitivity ◽  
False Alarm Probability ◽  
Navigation Systems ◽  
Fusion Algorithm ◽  
Chi Square ◽  
Filtering Algorithm ◽  
Anomalous Errors

To perform precise approach and landing concerning an aircraft in automatic mode, local airfield-based landing systems are used. For joint processing of measurements of the onboard inertial navigation systems (INS), altimeters and local landing systems, the Kalman filter is usually used. The application of the quadratic criterion in the Kalman filter entails the well-known problem of high sensitivity of the estimate to anomalous measurement errors. During the automatic approach phase, abnormal navigation errors can lead to disaster, so the data fusion algorithm must automatically identify and isolate abnormal measurements. This paper presents a recurrent filtering algorithm that is resistant to anomalous errors in measurements and considers its application in the data fusion problem for landing system measurements with onboard sensor measurements—INS and altimeters. The robustness of the estimate is achieved through the combined use of the least modulus method and the Kalman filter. To detect and isolate failures the chi-square criterion is used. It makes possible the customization of the algorithm in accordance with the requirements for false alarm probability and the alarm missing probability. Testing results of the robust filtering algorithm are given both for synthesized data and for real measurements.

Attitude Estimation By Separate-Bias Kalman Filter-Based Data Fusion

2004 ◽  
Vol 57 (2) ◽  
pp. 261-273 ◽  
Author(s):  
Peyman Setoodeh ◽  
Alireza Khayatian ◽  
Ebrahim Frajah
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Dynamic Modelling ◽  
Limited Range ◽  
Attitude Estimation ◽  
Fusion Algorithm ◽  
Experimental Implementation ◽  
Slow Drift ◽  
Error State ◽  
State Form

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

Fault Diagnosis and Reconfiguration for Mobile Robot Localization Based on Multi-Sensors Data Fusion

2021 ◽  
pp. 1-23
Author(s):  
Linda Hachemi ◽  
Mohamed Guiatni ◽  
Abedlkrim Nemra
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Mobile Robot ◽  
Fault Tolerant ◽  
Sensor Data ◽  
Robot Localization ◽  
Mobile Robot Localization ◽  
Estimation System ◽  
And Performance ◽  
Robot Position

In this paper, we propose a new approach for fault tolerant localization using multi-sensors data fusion for a unicycle-type mobile robot. The main contribution of this paper is a new architecture proposal for fault diagnosis and reconfiguration for mobile robot localization using multi-sensors data fusion and the duplication/comparison approach. Four different sensors usually embedded in mobile robots (Camera, IMU, GPS, and Odometer) are considered, while six different sensors couples combinations are used for sensor data fusion and the duplication of the localization and estimation system. In order to reach this aim, three different filters (EKF, SVSF, and ASVSF) have been proposed and compared. For each selected filter, a comparison mechanism is then introduced to compute different residuals by comparing the estimated robot position for each sensor couples separately. Faults are then detected using the structural residual diagnosis method. This approach assumes the occurrence of a single fault at a given time. A reconfiguration mechanism is then applied by selected the healthy sensors couple and their corresponding fusion filter. Several scenarios are considered for navigation-based fault tolerant localization approaches. Simulation results are presented to illustrate the advantage and performance of the proposed architecture. The proposed solutions are implemented and validated successfully using the V-REP simulator.

THE EXTENDED KALMAN FILTER AUGMENTED BY AN ADAPTIVE DIGITAL FILTER FOR DATA FUSION OF A MOBILE ROBOT CONTROL SYSTEM

2020 ◽  
Vol 2 ◽  
pp. 85-94
Author(s):  
P.A. BEZMEN
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Data Fusion ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Digital Filter ◽  
Robot Control ◽  
Mobile Robot Control ◽  
Operational Error ◽  
Robot Control System

The paper proposes the combination of the extended Kalman filter and an adaptive digital filter to compensate an operational error of the extended Kalman filter during data fusion of a mobile robot control system. The paper describes the structure and operation of such combination, shows the buffer memory configuration of an adaptive digital filter.

A SENSOR DATA FUSION ALGORITHM FOR INDOOR SLAM USING A LOW-COST MOBILE ROBOT

2012 ◽  
pp. 762-769
Author(s):  
LUCIANO BUONOCORE ◽  
AEROLINO DE ALMEIDA NETO ◽  
CAIRO LÚCIO NASCIMENTO JÚNIOR
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Low Cost ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion

scholarly journals Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Huadong Wang ◽  
Shi Dong
Keyword(s):  
Kalman Filter ◽  
Data Fusion ◽  
Observational Data ◽  
Measurement System ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Fusion Algorithm ◽  
Multisensor Data Fusion ◽  
Multiple Sensors ◽  
Multisensor Data

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

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