Optimal strapdown attitude integration algorithms

Ignagni, Mario

doi:10.2514/3.20558

Cited by 124 publications

(93 citation statements)

References 1 publication

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“…The word strapdown means that the angular velocity is obtained using gyroscopes strapped to an object. A number of integration methods have been described (IGNAGNI, 1990;BORTZ, 1971;JIANG and LIN, 1992). The orientation of the sensor with respect to the global co-ordinate frame is expressed with a rotation matrix, containing the three unit column vectors of the global co-ordinate system expressed in the sensor co-ordinate system (5).…”

Section: Model Of Sensor Signalsmentioning

confidence: 99%

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Measuring orientation of human body segments using miniature gyroscopes and accelerometers

Luinge

Veltink

2005

Med. Biol. Eng. Comput.

560

378

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In the medical field, there is a need for small ambulatory sensor systems for measuring the kinematics of body segments. Current methods for ambulatory measurement of body orientation have limited accuracy when the body moves. The aim of the paper was to develop and validate a method for accurate measurement of the orientation of human body segments using an inertial measurement unit (IMU). An IMU containing three single-axis accelerometers and three single-axis micromachined gyroscopes was assembled in a rectangular box, sized 20 x 20 x 30 mm. The presented orientation estimation algorithm continuously corrected orientation estimates obtained by mathematical integration of the 3D angular velocity measured using the gyroscopes. The correction was performed using an inclination estimate continuously obtained using the signal of the 3D accelerometer. This reduces the integration drift that originates from errors in the angular velocity signal. In addition, the gyroscope offset was continuously recalibrated. The method was realised using a Kalman filter that took into account the spectra of the signals involved as well as a fluctuating gyroscope offset. The method was tested for movements of the pelvis, trunk and forearm. Although the problem of integration drift around the global vertical continuously increased in the order of 0.50 degrees s(-1), the inclination estimate was accurate within 3 degrees RMS. It was shown that the gyroscope offset could be estimated continuously during a trial. Using an initial offset error of 1 rad s(-1), after 2 min the off-set error was roughly 5% of the original offset error. Using the Kalman filter described, an accurate and robust system for ambulatory motion recording can be realised.

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Section: Model Of Sensor Signalsmentioning

confidence: 99%

“…Change in orientation can be estimated by integrating the angular velocity according to an algorithm such as given by Bortz or Ignagni (BORTZ, 1971;IGNAGNI, 1990). However, an error in measured angular velocity will result in increasing inaccuracy in the estimated orientation.…”

Section: Introductionmentioning

confidence: 99%

Measuring orientation of human body segments using miniature gyroscopes and accelerometers

Luinge

Veltink

2005

Med. Biol. Eng. Comput.

560

378

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“…MARG systems or AHRS (Attitude and Heading Reference Systems) are able to provide a complete measurement of orientation relative to the direction of gravity and the earth's magnetic field. A gyroscope measures angular velocity which, sensor orientation will be computed over the time if initial conditions are known (Bortz, 1971;Ignagni, 1990). Precision gyroscopes are really expensive and grave for most applications while low accuracy MEMS (Micro Electrical Mechanical System) devices are used in a majority of applications (Yazdi et al 1998).…”

Section: Introductionmentioning

confidence: 99%

Experimental Comparison Between Mahoney and Complementary Sensor Fusion Algorithm for Attitude Determination by Raw Sensor Data of Xsens Imu on Buoy

Jouybari

Ardalan

Rezvani

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The accurate measurement of platform orientation plays a critical role in a range of applications including marine, aerospace, robotics, navigation, human motion analysis, and machine interaction. We used Mahoney filter, Complementary filter and Xsens Kalman filter for achieving Euler angle of a dynamic platform by integration of gyroscope, accelerometer, and magnetometer measurements. The field test has been performed in Kish Island using an IMU sensor (Xsens MTi-G-700) that installed onboard a buoy so as to provide raw data of gyroscopes, accelerometers, magnetometer measurements about 25 minutes. These raw data were used to calculate the Euler angles by Mahoney filter and Complementary filter, while the Euler angles collected by XSense IMU sensor become the reference of the Euler angle estimations. We then compared Euler angles which calculated by Mahoney Filter and Complementary Filter with reference to the Euler angles recorded by the XSense IMU sensor. The standard deviations of the differences between the Mahoney Filter, Complementary Filter Euler angles and XSense IMU sensor Euler angles were about 0.5644, 0.3872, 0.4990 degrees and 0.6349, 0.2621, 2.3778 degrees for roll, pitch, and heading, respectively, so the numerical result assert that Mahoney filter is precise for roll and heading angles determination and Complementary filter is precise only for pitch determination, it should be noted that heading angle determination by Complementary filter has more error than Mahoney filter.

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“…in which c  is the integral of the cross product, and it can be approximated as a noncommutative rate vector of the rotation vector [2,6]:…”

Section: Screw Blade Algorithmmentioning

confidence: 99%

“…The strapdown inertial navigation system (SDINS) algorithm comprises attitude updating, velocity updating, and position updating, among which attitude updating usually utilizes the rotation vector algorithm [1][2][3][4][5][6][7]. The procedure of the rotation vector algorithm is: first, divide the attitude updating time interval equally into several segments and calculate the angular increment of each segment respectively; then, compute the total angular increment during the entire updating interval; finally, figure out the direction cosine matrix/quaternion of the updating time interval from the total angular increment.…”

Section: Introductionmentioning

confidence: 99%

Strapdown Navigation Using Geometric Algebra: Screw Blade Algorithm

Wu¹,

Wang²

2012

POS

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The rigid body motion can be represented by a motor in geometric algebra, and the motor can be rewritten as a trinometric function of the screw blade. In this paper, a screw blade strapdown inertial navigation system (SDINS) algorithm is developed. The trigonometric function form of the motor is derived and utilized to deduce the Bortz equation of the screw blade. The screw blade SDINS algorithm is proposed by using the procedure similar to that of the conventional rotation vector attitude updating algorithm. The superiority of the screw blade algorithm over the conventional ones in precision is analyzed. Simulation results reveal that the screw blade algorithm is more suitable for the high-precision SDINS than the conventional ones.

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Optimal strapdown attitude integration algorithms

Cited by 124 publications

References 1 publication

Measuring orientation of human body segments using miniature gyroscopes and accelerometers

Measuring orientation of human body segments using miniature gyroscopes and accelerometers

Experimental Comparison Between Mahoney and Complementary Sensor Fusion Algorithm for Attitude Determination by Raw Sensor Data of Xsens Imu on Buoy

Strapdown Navigation Using Geometric Algebra: Screw Blade Algorithm

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