3B kamera takibi için eylemsizlik algılayıcılarının birleştirilmesi

Name: 3B kamera takibi için eylemsizlik algılayıcılarının birleştirilmesi
Author: Özer, N., Erdem, Tanju, Ercan, Ali Özer, Eroğlu Erdem, Ç.

İsim	3B kamera takibi için eylemsizlik algılayıcılarının birleştirilmesi
Yazar	Özer, N., Erdem, Tanju, Ercan, Ali Özer, Eroğlu Erdem, Ç.
Basım Tarihi:	2012
Basım Yeri	- IEEE
Konu	Bayes methods, Kalman filters, Accelerometers, Cameras, Gyroscopes, Inertial systems, Nonlinear filters, Sensor fusion
Tür	Belge
Dil	Türkçe
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4673-0054-4
Kayıt Numarası	62c1abb0-3e06-4556-8830-8f5c2f812b06
Lokasyon	Electrical & Electronics Engineering, Computer Science
Tarih	2012
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	It is well known in a Bayesian filtering framework, the use of inertial sensors such as accelerometers and gyroscopes improves 3D tracking performance compared to using camera measurements only. The performance improvement is more evident when the camera undergoes a high degree of motion. However, it is not well known whether the inertial sensors should be used as control inputs or as measurements. In this paper, we present the results of an extensive set of simulations comparing different combinations of using inertial sensors as control inputs or as measurements. We show that it is better use a gyroscope as a control input while an accelerometer can be used as a measurement or control input. We also derive and present the extended Kalman filter (EKF) equations for a specific case of fusing accelerometer and gyroscope data that has not been reported before.
DOI	10.1109/SIU.2012.6204725

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

3B kamera takibi için eylemsizlik algılayıcılarının birleştirilmesi

Yazar Özer, N., Erdem, Tanju, Ercan, Ali Özer, Eroğlu Erdem, Ç.

Basım Tarihi 2012

Basım Yeri - IEEE

Konu Bayes methods, Kalman filters, Accelerometers, Cameras, Gyroscopes, Inertial systems, Nonlinear filters, Sensor fusion

Tür Belge

Dil Türkçe

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4673-0054-4

Kayıt Numarası 62c1abb0-3e06-4556-8830-8f5c2f812b06

Lokasyon Electrical & Electronics Engineering, Computer Science

Tarih 2012

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin It is well known in a Bayesian filtering framework, the use of inertial sensors such as accelerometers and gyroscopes improves 3D tracking performance compared to using camera measurements only. The performance improvement is more evident when the camera undergoes a high degree of motion. However, it is not well known whether the inertial sensors should be used as control inputs or as measurements. In this paper, we present the results of an extensive set of simulations comparing different combinations of using inertial sensors as control inputs or as measurements. We show that it is better use a gyroscope as a control input while an accelerometer can be used as a measurement or control input. We also derive and present the extended Kalman filter (EKF) equations for a specific case of fusing accelerometer and gyroscope data that has not been reported before.

DOI 10.1109/SIU.2012.6204725