Real-Time Tracking for Augmented reality

Badeche, Mounira; Benmohamed, Mohamed

doi:10.1109/ictta.2006.1684654

Cited by 3 publications

(2 citation statements)

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“…Each button, on clicking changes the scene. Each scene has different color filters and the scene includes additional sliders to twitch the hue within the set range [2]. The application hosts a post processing utility tool that is available on Unity asset store.…”

Section: Methods and Proceduresmentioning

confidence: 99%

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2019

Madridge J Bioinform Syst Biol

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Section: Methods and Proceduresmentioning

confidence: 99%

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2019

Madridge J Bioinform Syst Biol

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“…Inertial Measurement Unit (IMU) sensors are an essential component in various applications involving movement and orientation monitoring, their implementation is mostly for unmanned aircraft [1][5] [12][13], augmented reality [2][3] [11 ] [14], robotics [4] [7][8] [9], and autonomous vehicles [6], However, the data provided by IMU sensors tends to be polluted by interference and noise, resulting in less accurate estimated values. In this context, the Kalman Filter algorithm is an effective solution to improve the quality of IMU sensor data and make more reliable predictions [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

Mobed Bachtiar,

Wibowo,

Rifa’I

et al. 2023

Int. J. Artif. Intell. Robot.

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The Inertial Measurement Unit (IMU) sensor is a tool used to measure the speed and acceleration of an object in 3 dimensions (x, y, z). IMU sensors are often used in robotics, drone control, autonomous vehicles, and augmented reality applications. Usually, the data obtained from the IMU sensor is contaminated by interference and noise, which can reduce measurement accuracy. Kalman Filter is a statistical method used to combine measurement data with a mathematical system model to produce better estimates. In the IMU context, the Kalman Filter removed interference and noise affecting acceleration and speed data so that IMU sensor data could be estimated more accurately. This algorithm predicts the next data state based on previous data and updates the prediction with new measurement data. The measurement implementation in this research is the IMU sensor on the GY-91 module to determine the object's tilt on the pitch, roll, and yaw axes during flight. The ARM STM32F407VGT6 microcontroller pin reads the sensor, and then the estimation and prediction process is carried out using the Kalman filter algorithm. With the parameters Kalman Measurement Error = 1, Estimation Error = 0.12, and Covariance Process = 0.4, it can predict the reading results from the IMU sensor well.

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Real-Time Corner Detection on Mobile Platforms Using Cuda

Chahuara

Rodríguez

2018

2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)

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Real-Time Tracking for Augmented reality

Cited by 3 publications

References 4 publications

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Estimation of Axis Roll Pitch of GY-91 IMU Sensor Reading Using Kalman Filter

Real-Time Corner Detection on Mobile Platforms Using Cuda

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