Kalman Filter Realization for Orientation and Position Estimation on Dedicated Processor

Romaniuk, Slawomir; Gosiewski, Zdzisław

doi:10.2478/ama-2014-0016

Cited by 24 publications

(12 citation statements)

References 21 publications

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“…The forth algorithm is a Kalman filter [34] [16]. The Kalman filter takes a statistical approach to estimate the state(s) based on inputs and past observations.…”

Section: Algorithm #4: Kalman Filtermentioning

confidence: 99%

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Milam¹

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Most small unmanned aerial systems in use today, employ extended Kalman filter sensor fusion algorithms in order to provide accurate estimations of attitude or orientation. These complex algorithms use measurements from GPS receivers and magnetometer sensors that can be rendered useless in GPS denied environments or areas of significant magnetic interference, such as inside buildings or other structures. The complexity of these algorithms makes them inaccessible for some researchers and hobbyists who wish to code their own attitude estimation algorithms. This complexity is also computationally expensive and requires processors that are powerful enough to operate the algorithms along with any command and control functions required by the application. In contrast, there are simple sensor fusion algorithms such as the complementary filter or linear Kalman filter, that are commonly used by hobbyists because they are relatively easy to implement and computationally lightweight. However, these methods are not as accurate as the extended Kalman filter and therefore, are not adequate for some of the emerging precision applications in aerial robotics. The goal of this research is to investigate an attitude estimation algorithm that uses two separate inertial measurement units (IMUs), each consisting of tri-axis accelerometers and tri-axis gyroscopes. This dual or twin IMU (TIMU) algorithm is compared to several common algorithms that only use one IMU, such as the complementary filter and linear Kalman filter. Analysis of a one degree of freedom experiment shows that the TIMU algorithm provides a more accurate attitude estimate. The analysis also shows that distance between the IMU and the rotating body's center of gravity can have an inverse effect on attitude accuracy. The ability of the algorithms to provide an accurate estimate of the rate of attitude change is used as a performance metric, in addition to the accuracy of attitude estimates. The complexity of the twin IMU algorithm is kept to a minimum. It is presented in a way that can be easily programed by the layman and has a small computational footprint.

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“…The forth algorithm is a Kalman filter [34] [16]. The Kalman filter takes a statistical approach to estimate the state(s) based on inputs and past observations.…”

Section: Algorithm #4: Kalman Filtermentioning

confidence: 99%

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Milam¹

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“…The acceleration sensor returns the acceleration values along the 3 axis (a x , a y , a z ). Based on this data and the appropriate trigonometric operations [2], the roll and pitch angles defining the rotation of the quadcopter along the X and Y axis can be determined. The obtained acceleration rates (a y , a z ) are shown in Fig.…”

Section: Accelerometermentioning

confidence: 99%

Implementation of Control System and Tracking Objects in a Quadcopter System

Ariram

Röning

Kowalczuk

2019

Lecture Notes in Computer Science

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In this paper, we implement a quadcopter assembly with control and navigation module. The project also includes the design of the control panel for the operator which consists of a set of the microcontroller and the glove equipped with sensors and buttons. The panel has a touch screen which displays current parameters such as vehicle status, including information about orientation and geographical coordinates. The concept of quadcopter control is based on the movement of the operator hand. In addition, we have included the object detection for detecting the objects from the quadcopter view of point. To detect an object, we need to have some idea of where the object may be and how the image is divided into segments. It creates a kind of chicken and egg problem, where we must recognize the shape (and class) of the object knowing its location and recognize the location of the object knowing its shape. Some visual characteristics such as clothing and the human face, they can be part of the same subject, but it is difficult to recognize this without recognizing the object first.

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“…Therefore, a proper fusion of IMU data and the algorithm to compensate for external acceleration is needed to overcome the shortcomings of each sensor and the effect of external acceleration. The fusion technique evolved along two major paths: one approach incorporates the use of a Kalman filter [10][11][12] while the other algorithm consists of a complementary filter [13,14].…”

Section: Introductionmentioning

confidence: 99%

Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

Widodo

Wada

2016

Journal of Sensors

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Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

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Kalman Filter Realization for Orientation and Position Estimation on Dedicated Processor

Cited by 24 publications

References 21 publications

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Design and Evaluation of Novel Attitude Estimation System Using MEMS Sensors for Indoor UAS

Implementation of Control System and Tracking Objects in a Quadcopter System

Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

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