Performances comparison of different MEMS-based IMUs

Agostino, Mattia De; Manzino, Ambrogio; Piras, Marco

doi:10.1109/plans.2010.5507128

Cited by 33 publications

(25 citation statements)

References 3 publications

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“…The results of the velocity random walk of the sensors identified in the short cluster times corresponds to the catalog values of noise density of the sensors in their datasheets. Also identified curve shape of the ADXL203 device with capacitive system has good agreement with the shapes already published for capacitive accelerometers inside INS and IMUs [6], [7]. …”

Section: Measurement Setupsupporting

confidence: 85%

“…specific improvements inside the MEMS structure. There were many separate analysis of inertial measurement units (IMUs) and inertial navigation systems (INS) published in last decade with respect to noise performance and stability [2], [6], [7], [8]. But no direct comparison of noise and stability properties of MEMS devices with major principles on the market has been carried out yet.…”

Section: Introductionmentioning

confidence: 99%

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Allan variance analysis on MEMS tilt sensors with different principles of operation

Havránek

Klusáček

Beneš

et al. 2011

2011 IEEE SENSORS Proceedings

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Tilt sensing is an important part in absolute position and orientation measurement, e.g. inertial navigation units and tilt detectors. In this paper, the direct comparison of three MEMS tilt sensors with different principle of operation has been carried out and evaluated using Allan variance (AV) method. Selected samples represent major principles used in MEMS accelerometers on the market -capacitive Analog Devices ADXL203, thermal MEMSIC MXA6500M and piezoresistive Panasonic AGS61231. All devices have similar measurement range and acceleration sensitivity. The results of AV analysis show similar behavior and curve shape in capacitive principle, with the lowest acceleration random walk observed, and in thermal principle, with six times higher bias instability in compare with capacitive principle. The piezoresistive principle has lower velocity random walk, but it is very sensitive to temperature variations, seen in long cluster times.

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Section: Measurement Setupsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Allan variance analysis on MEMS tilt sensors with different principles of operation

Havránek

Klusáček

Beneš

et al. 2011

2011 IEEE SENSORS Proceedings

View full text Add to dashboard Cite

show abstract

“…However, this type of technology can be highly prone to drift. According to De Agostino et al [145], who carried out a comparative study on different MEMS-based IMUs, the largest drift errors are related to inertial sensor imperfections of the gyro and accelerometer. Bikonis et al [146] integrated surface GPS positioning with an IMU, using an external Kalman filter, which produced more accurate positioning and Ko et al [147] propose a system using a combination of depth measurement and MEMS-based IMU to enhance the accuracy attitude estimation.…”

Section: Navigation and Positioning Sensorsmentioning

confidence: 99%

Inspection-Class Remotely Operated Vehicles—A Review

Capocci

Dooly

Omerdić

et al. 2017

JMSE

182

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This paper presents a review of inspection-class Remotely Operated Vehicles (ROVs). The review divides the classification of inspection-class ROVs; categorising the vehicles in order of size and capability. A state of the art technology review is undertaken, discussing various common subsystems of the ROV. Standard and novel ROV shapes and designs are reviewed, with emphasis on buoyancy, frame materials and hydrodynamics. Several power considerations and designs are discussed, accounting for battery fed and mains fed systems. ROV telemetry is split into a discussion on the various transmission hardware systems and the communication protocols that are most widely used in industry and research today. A range of thruster technologies is then introduced with consideration taken of the various thruster architectures available. Finally, the navigation and positioning sensors employed for ROV navigation and control are reviewed. The author has also created a number of comparison tables throughout the review; tables include comparison of wired data transmission technology, comparison of common ROV communication protocols and comparisons of various inertial navigation systems. By the end of the review the reader will have clearer understanding on the fundamentals of inspection-class ROV technologies and can use this as an introduction to further paper investigation.

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“…While reductions in size, power consumption, and cost of MEMS IMUs are occurring, their higher sensor noise levels and inaccuracy make them insufficient for many navigation applications compared to other high-end inertial navigation systems (INS). For example, compared to a commercial grade INS (that costs on the order of tens of thousands of dollars, power consumption on the order of tens of watts, exhibits a drift of 0.001 -1 o /hr for a fiber optic gyroscope, and 0.001 -1 mg of acceleration bias for a pendulus accelerometer), a MEMS IMU gyroscope drifts greater than 60 o /hr, and its accelerometer exhibits 0.01 -1 mg of bias [12], but it only costs on the order of tens of dollars and consumes power on the order of tens of milliwatts.…”

Section: Navigation With Inertial Measurement Units (Imu)mentioning

confidence: 99%

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

Kepper¹

2017

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Recent advances in acoustic navigation methodologies are enabling the way for AUVs to extend their submerged mission time and maintain a bounded XY position error. Additionally, advances in inertial sensor technology have drastically lowered the size, power consumption, and cost of these sensors. Nonetheless, these sensors are still noisy and accrue error over time. This thesis builds on the research and recent developments in single beacon one-waytravel-time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying mostly on a consumer grade microelectromechanical system (MEMS) inertial measurement unit (IMU) and a vehicle's dynamic model velocity. An implementation of an Extended Kalman Filter (EKF) that includes IMU bias estimation and coupled with a range filter, is obtained in the field on two OceanServer Technology, Inc. Iver2 AUVs and one Bluefin Robotics SandShark AUV. Results from these field trials on Ashumet Pond of Falmouth, Massachusetts, the Charles River of Cambridge, Massachusetts, and Monterey Bay near Santa Cruz, California show a navigation solution accuracy comparable to current standard navigation techniques.

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Performances comparison of different MEMS-based IMUs

Cited by 33 publications

References 3 publications

Allan variance analysis on MEMS tilt sensors with different principles of operation

Allan variance analysis on MEMS tilt sensors with different principles of operation

Inspection-Class Remotely Operated Vehicles—A Review

MEMS IMU navigation with model based dead-reckoning and one-way-travel-time acoustic range measurements for autonomous underwater vehicles

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