Efficient inertially aided visual odometry towards mobile augmented reality

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2013

Author

Aksoy, Yağız

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With the increase in the number and computational power of commercial mobile devices like smart phones and tablet computers, augmented reality applications are gaining more and more volume. In order to augment virtual objects effectively in real scenes, pose of the camera should be estimated with high precision and speed. Today, most of the mobile devices feature cameras and inertial measurement units which carry information on change in position and attitude of the camera. In this thesis, utilization of inertial sensors on mobile devices in aiding visual pose estimation is studied. Error characteristics of the inertial sensors on the utilized mobile device are analyzed. Gyroscope readings are utilized for aiding 2D feature tracking while accelerometer readings are used to help create a sparse 3D map of features later to be used for visual pose estimation. Metric velocity estimation is formulated using inertial readings and observations of a single 2D feature. Detailed formulations of uncertainties on all the estimated variables are provided. Finally, a novel, lightweight filter, which is capable of estimating the pose of the camera together with the metric scale, is proposed. The proposed filter runs without any heuristics needed for covariance propagation, which enables it to be used in different devices with different sensor characteristics without any modifications. Sensors with poor noise characteristics are successfully utilized to aid the visual pose estimation.

Subject Keywords

Image processing, Imaging systems, Mobile communication systems., Inertial navigation., Augmented reality.

URI

http://etd.lib.metu.edu.tr/upload/12616322/index.pdf
https://hdl.handle.net/11511/23035

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

Y. Aksoy, “Efficient inertially aided visual odometry towards mobile augmented reality,” M.S. - Master of Science, Middle East Technical University, 2013.