Embedded Sensor Fusion System for Unmanned Vehicle Navigation

Scherz, P.; Haderer, Andreas; Pourvoyeur, K.; Stelzer, Andreas

doi:10.1109/mesa.2008.4735745

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Various fusion methods have been used in previous works just like ad hoc [34], dead reckoning [35], fuzzy logic [36], Kalman filter [6] and its variations [10], neural networks [2], or numerical discretization [37]. Most of these sensor fusion systems are usually developed for embedded systems just like the ones that used Field-Programmable Gate Arrays (FPGA) with digital signal processors (DSP) [38].…”

Section: Introductionmentioning

confidence: 99%

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Vista

Chong

2019

Journal of Sensors

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This paper describes the design and real-time implementation of a proposed algorithm for deriving an accurate heading system by fusing data from various inexpensive sensor devices that is comparable to more expensive maritime navigation systems. The proposed algorithm is a 3-Stage Classification N’ Weighing (CnW) Heading System with forward azimuth (FAz) and extended Kalman filter (EKF). Data from three Global Positioning System devices, an inertial measurement unit, and an electronic compass were fed into the algorithm that can be generally described as Classification N’ Weighing-Stage 1 → forward azimuth → Classification N’ Weighing-Stage 2 → extended Kalman filter → Classification N’ Weighing-Stage 3. The proposed algorithm is shown to be comparably accurate as an expensive marine navigation system, and it has less processing time compared to our previous work. The Qt-anywhere-based system developed on a Linux desktop was successfully downloaded onto an Ubuntu Linux-embedded board for real-time implementation. Important notes related to device naming problems when deploying the system on a Linux-embedded board are also given as reference for those interested to address it.

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Section: Introductionmentioning

confidence: 99%

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Vista

Chong

2019

Journal of Sensors

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“…These sensor fusion systems, especially the navigation systems, are usually deployed on embedded systems similar to ones that utilized field-programmable gate arrays (FPGA) with digital signal processors (DSP) [32]. The work presented in this paper details the development and deployment of a real-time sensor fusion system on an embedded board based on a previously proposed system of ours that was just done postprocessing [33].…”

Section: Introductionmentioning

confidence: 99%

Design, Development, and Deployment of Real-Time Sensor Fusion (CnW + EKF) for a Linux-Based Embedded System Using Qt-Anywhere

Vista

Chong

2018

Journal of Sensors

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This paper describes the design, development, and implementation of a real-time sensor fusion system that utilizes the classification and weighing plus extended Kalman filter algorithm to derive heading for navigation using inexpensive sensors. This algorithm was previously tested only through postprocessing using MATLAB and is now reprogrammed using Qt and deployed on a Linux-based embedded board for real-time operation. Various data from inexpensive sensors such as global positioning system devices, an electronic compass, and an inertial measurement unit were utilized to ultimately derive a more reliable and accurate heading value. The algorithm flow can be described with the GPS values first being evaluated and classified which are then fused with the EC heading using classification and weighing, whose result is then passed through an EKF to fuse with the IMU data. Real-time tests and trials were done to prove the operational capability of the developed process. The complete setup and configuration processes of the systems for development and deployment via Qt are also provided for those interested to replicate the process.

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“…At the same time, the errors of GPS system does not accumulate over time. References [2][3][4][5][6] have studied MEMS-IMU and lowcost GPS integrated navigation system. In [7][8], researchers use a barometric altimeter to assist the calculation of the inertial altitude path.…”

Section: Introductionmentioning

confidence: 99%

A Study on High Precision MEMS-IMU/GPS/Magnetometer/Barometer Integrated Navigation System

Wang¹,

Qiu²

2018

2018 International Conference on Computer Science and Biomedical Engineering (CSBIOE 2018)

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Abstract. Considering the urgent need of low cost, high performance and high precision navigation system, a MEMS-IMU / GPS / magnetometer / barometer integrated navigation system based on DSP is designed. Using the output of MEMS-IMU, we can obtain the carrier's attitude, velocity and position with SINS algorithm. We use a barometer to dampen the inertial altitude path. Then combined with the information of GPS and magnetometer, an integrated navigation algorithm based on Kalman filter is designed, We conduct static and dynamic experiments on the integrated navigation system and the results show that the system can achieve high navigation accuracy.

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Embedded Sensor Fusion System for Unmanned Vehicle Navigation

Cited by 5 publications

References 4 publications

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Design and Real-Time Implementation of a 3-Stage CnW Heading System on an Ubuntu Linux-Embedded Board

Design, Development, and Deployment of Real-Time Sensor Fusion (CnW + EKF) for a Linux-Based Embedded System Using Qt-Anywhere

A Study on High Precision MEMS-IMU/GPS/Magnetometer/Barometer Integrated Navigation System

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