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

العنوان Fusing inertial sensor data in an extended kalman filter for 3D camera tracking
المؤلف Erdem, Tanju, Ercan, Ali Özer
تاريخ النشر: 2015-02
مكان النشر - IEEE
الموضوع Inertial sensor fusion, Extended Kalman filter, 3D camera tracking, inertial measurement unit, Accelerometer, Gyroscope
النوع دورية
اللغة الإنجليزية
رقمي نعم
مخطوط لا
المكتبة: جامعة اوزيجين
معرف أصل المكتبة 1057-7149
رقم السجل e9c60303-0810-44a0-877d-6146c663f85c
موقع المكتبة Electrical & Electronics Engineering, Computer Science
التاريخ 2015-02
ملاحظات Due to copyright restrictions, the access to the full text of this article is only available via subscription.
نص عينة 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
عرض في المصدر جامعة اوزيجين Özyeğin Üniversitesi
Özyeğin Üniversitesi جامعة اوزيجين

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

المؤلف Erdem, Tanju, Ercan, Ali Özer
تاريخ النشر 2015-02
مكان النشر - IEEE
الموضوع Inertial sensor fusion, Extended Kalman filter, 3D camera tracking, inertial measurement unit, Accelerometer, Gyroscope
النوع دورية
اللغة الإنجليزية
رقمي نعم
مخطوط لا
المكتبة جامعة اوزيجين
معرف أصل المكتبة 1057-7149
رقم السجل e9c60303-0810-44a0-877d-6146c663f85c
موقع المكتبة Electrical & Electronics Engineering, Computer Science
التاريخ 2015-02
ملاحظات Due to copyright restrictions, the access to the full text of this article is only available via subscription.
نص عينة 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
Özyeğin Üniversitesi
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