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Proceedings Paper

Fusion of inertial and vision data for accurate tracking
Author(s): Jing Chen; Wei Liu; Yongtian Wang; Junwei Guo
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Paper Abstract

We present a sensor fusion framework for real-time tracking applications combining inertial sensors with a camera. In order to make clear how to exploit the information in the inertial sensor, two different fusion models gyroscopes only model and accelerometers model are presented under extended Kalman filter framework. Gyroscopes only model uses gyroscopes to support the vision-based tracking without considering acceleration measurements. Accelerometers model utilizes both measurements from the gyroscopes, accelerometers and vision data to estimate the camera pose, velocity, acceleration and sensor biases. Synthetic data and real image experimental sequences show dramatic improvements in tracking stability and robustness of estimated motion parameters for gyroscope model, when the accelerometer measurements exist drift.

Paper Details

Date Published: 14 January 2012
PDF: 8 pages
Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 83491D (14 January 2012); doi: 10.1117/12.920343
Show Author Affiliations
Jing Chen, Beijing Institute of Technology (China)
Wei Liu, Beijing Institute of Technology (China)
Yongtian Wang, Beijing Institute of Technology (China)
Junwei Guo, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 8349:
Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis
Zhu Zeng; Yuting Li, Editor(s)

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