Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Zmitri, Makia; Fourati, Hassen; Prieur, Christophe

doi:10.3390/s20205726

Cited by 16 publications

(8 citation statements)

References 41 publications

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“…In [6]- [10], the differential equation ( 1) was used to develop magenetic-field aided INS solutions. The resulting implementations achieve much lower error growth rate compared to stand-alone inertial navigation systems.…”

Section: A Related Workmentioning

confidence: 99%

A Tightly-Integrated Magnetic-Field aided Inertial Navigation System

Huang

Hendeby

Skog

2022

2022 25th International Conference on Information Fusion (FUSION)

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A tightly integrated magnetic-field aided inertial navigation system is presented. The system uses a magnetometer sensor array to measure spatial variations in the local magneticfield. The variations in the field are -via a recursively updated polynomial magnetic-field model -mapped into displacement and orientation changes of the array, which in turn are used to aid the inertial navigation system. Simulation results show that the resulting navigation system has three orders of magnitude lower position error at the end of a 40 seconds trajectory as compared to a standalone inertial navigation system. Thus, the proposed navigation solution has the potential to solve one of the key challenges faced with current magnetic-field simultaneous localization and mapping (SLAM) systems -the very limited allowable length of the exploration phase during which unvisited areas are mapped.

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Section: A Related Workmentioning

confidence: 99%

A Tightly-Integrated Magnetic-Field aided Inertial Navigation System

Huang

Hendeby

Skog

2022

2022 25th International Conference on Information Fusion (FUSION)

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“…Magnetic fluctuations cannot be generated by Equation (18) in the simulation environment. However, random magnetic perturbations commonly occur in real world scenarios, where the magnetometer output gets disturbed by ferromagnetic objects [ 42 ]. Therefore, an essential requirement is to simulate random magnetic fluctuations in the proposed test environment.…”

Section: Test Environmentmentioning

confidence: 99%

An Open-Source Test Environment for Effective Development of MARG-Based Algorithms

Odry

2021

Sensors

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This paper presents an open-source environment for development, tuning, and performance evaluation of magnetic, angular rate, and gravity-based (MARG-based) filters, such as pose estimators and classification algorithms. The environment is available in both ROS/Gazebo and MATLAB/Simulink, and it contains a six-degrees of freedom (6 DOF) test bench, which simultaneously moves and rotates an MARG unit in the three-dimensional (3D) space. As the quality of MARG-based estimation becomes crucial in dynamic situations, the proposed test platform intends to simulate different accelerating and vibrating circumstances, along with realistic magnetic perturbation events. Moreover, the simultaneous acquisition of both the real pose states (ground truth) and raw sensor data is supported during these simulated system behaviors. As a result, the test environment executes the desired mixture of static and dynamic system conditions, and the provided database fosters the effective analysis of sensor fusion algorithms. The paper systematically describes the structure of the proposed test platform, from mechanical properties, over mathematical modeling and joint controller synthesis, to implementation results. Additionally, a case study is presented of the tuning of popular attitude estimation algorithms to highlight the advantages of the developed open-source environment.

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“…After presenting the Openshoe method [ 4 ] based on the Pedestrian Dead Reckoning (PDR) system with the foot-mounted IMU, and by emerging the Zero-Velocity Potential Update (ZUPT) method in [ 5 ], numerous Kalman Filter (KF) and GPS-based indoor localization methods have been presented. For example, online calibration of INS/ZUPT using Extended Kalman Filter (EKF) [ 6 ], Constrained Square-Root Unscented Kalman Filter (CSR-UKF) and UKF methods [ 7 , 8 ], magnetic field Gradient-based EKFs [ 9 ] are evaluated and discussed. Moreover, various tightly and loosely coupled integrations of indoor PDR systems are designed and evaluated using Bluetooth [ 10 ], GPS [ 11 ], and Radio Frequency Identification (RFID) [ 12 ], which showed a more accurate performance compared to the stand-alone INS and Openshoe methods.…”

Section: Related Workmentioning

confidence: 99%

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Farhangian

Sefidgar

Landry

2021

Sensors

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The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.

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Magnetic Field Gradient-Based EKF for Velocity Estimation in Indoor Navigation

Cited by 16 publications

References 41 publications

A Tightly-Integrated Magnetic-Field aided Inertial Navigation System

A Tightly-Integrated Magnetic-Field aided Inertial Navigation System

An Open-Source Test Environment for Effective Development of MARG-Based Algorithms

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

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