Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform

Gośliński, Jarosław; Nowicki, Michał; Skrzypczyński, Piotr

doi:10.1109/jsen.2015.2397397

Cited by 42 publications

(27 citation statements)

References 20 publications

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“…Recently, Gośliński, 4 Michel, 5 and Nowicki 6 compared various orientation algorithms on Android phones, with varying results. Nowicki, and Gośliński both utilized an Xsens system for determining ground truth while Michel used a motion capture system capable of 0.5 • accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Smart phone orientation estimation comparisons using three axis gimbal

Gaquin

Fields

2016

SPIE Proceedings

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Smartphones have put powerful sensor arrays in nearly everyone's pockets. Fusing the data from these sensors it is possible to estimate the phone's current orientation. In this study we utilize a 3 axis gimbal to compare the performance of multiple orientation estimation algorithms. Controlling the position of the gimbal allows us to compare the known device orientation to the estimated orientation. Using this same method we determine where each algorithm's faults lie, and where they begin to break down. Then repeating these movements we are able to compare each algorithm to each other.

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Section: Related Workmentioning

confidence: 99%

“…The other algorithms were: the algorithm defined in Gośliński et al, 4 the Madgwick algorithm, 9 and the Mahony algorithm. 10 Rather than show the proofs for each algorithm here, we will give some insight to each algorithm and ask you see their publications for more detail.…”

Section: Orientation Algorithmsmentioning

confidence: 99%

Smart phone orientation estimation comparisons using three axis gimbal

Gaquin

Fields

2016

SPIE Proceedings

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“…The rotation and the orientation of the device can be determined based on the readings from these sensors and therefore the head position relative to the screen can be estimated. The information from these sensors is delivered quickly (with frequency of 200 Hz on Android devices) and do not require time consuming computations [19]. Moreover, the tracking range of the sensors is not limited by the camera field of view.…”

Section: Introductionmentioning

confidence: 99%

“…Filtering of the sensor signals takes into account earlier sensor readings and generates the additional delay in the application response to the head movement. Yet, it may be unnoticeable with use of the Extended Kalman Filter [19]. Still, sensors like accelerometer or gyroscope are not able to notice the actual movement of the head itself, omission of which can destroy the 3D impression.…”

Section: Introductionmentioning

confidence: 99%

Fast GPU and CPU computing for Head Position Estimation

Szkudlarek¹,

Pietruszka²

2015

Annals of Computer Science and Information Systems

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Abstract-The head movement based control methods in the 3D graphic applications requires the real-time face position estimation. Therefore, the tracking method at the high speed and with the minimal latency is needed. This is especially hard to achieve when the face is tracked with the use of the high resolution video image on mobile devices. In the article, we present several methods for an acceleration of the face position estimation method based on the fuzzy skin color classifier and other color-based face tracking methods. The acceleration is achieved through a highly parallel GPU computation, the precalculation of the classifier weights and through the combined computations on the GPU and the CPU. The achieved computation time is independent of the used skin color classification method, allowing for use of very complex classifiers. The presented methods provides the robust head position tracking on the high resolution video image of 1920x1080 pixels, at 300 frames per second, on the mobile device with a low computing power.

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“…Modern mobile devices are in most cases equipped with a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer. The information from these sensors can be used to create a system estimating the orientation of a smartphone [10]. The orientation estimate can be later effectively used to enhance the WiFi measurement.…”

Section: Introduc Onmentioning

confidence: 99%

Application of the One-factor CIR Interest Rate Model to Catastrophe Bond Pricing under Uncertainty

Nowicki

2014

JAMRIS

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Performance Comparison of EKF-Based Algorithms for Orientation Estimation on Android Platform

Cited by 42 publications

References 20 publications

Smart phone orientation estimation comparisons using three axis gimbal

Smart phone orientation estimation comparisons using three axis gimbal

Fast GPU and CPU computing for Head Position Estimation

Application of the One-factor CIR Interest Rate Model to Catastrophe Bond Pricing under Uncertainty

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