Fusing inertial sensor data in an extended kalman filter for 3D camera tracking

Name: Fusing inertial sensor data in an extended kalman filter for 3D camera tracking
Author: Erdem, Tanju, Ercan, Ali Özer

İsim	Fusing inertial sensor data in an extended kalman filter for 3D camera tracking
Yazar	Erdem, Tanju, Ercan, Ali Özer
Basım Tarihi:	2015-02
Basım Yeri	- IEEE
Konu	Inertial sensor fusion, Extended Kalman filter, 3D camera tracking, inertial measurement unit, Accelerometer, Gyroscope
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1057-7149
Kayıt Numarası	e9c60303-0810-44a0-877d-6146c663f85c
Lokasyon	Electrical & Electronics Engineering, Computer Science
Tarih	2015-02
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.
DOI	10.1109/TIP.2014.2380176
Cilt	24

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Fusing inertial sensor data in an extended kalman filter for 3D camera tracking

Yazar Erdem, Tanju, Ercan, Ali Özer

Basım Tarihi 2015-02

Basım Yeri - IEEE

Konu Inertial sensor fusion, Extended Kalman filter, 3D camera tracking, inertial measurement unit, Accelerometer, Gyroscope

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1057-7149

Kayıt Numarası e9c60303-0810-44a0-877d-6146c663f85c

Lokasyon Electrical & Electronics Engineering, Computer Science

Tarih 2015-02

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

Örnek Metin In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.

DOI 10.1109/TIP.2014.2380176

Cilt 24