Relative Pose Estimation Algorithm with Gyroscope Sensor

Abstract

This paper proposes a novel vision and inertial fusion algorithm S2fM (Simplified Structure from Motion) for camera relative pose estimation. Different from current existing algorithms, our algorithm estimates rotation parameter and translation parameter separately. S2fM employs gyroscopes to estimate camera rotation parameter, which is later fused with the image data to estimate camera translation parameter. Our contributions are in two aspects. (1) Under the circumstance that no inertial sensor can estimate accurately enough translation parameter, we propose a translation estimation algorithm by fusing gyroscope sensor and image data. (2) Our S2fM algorithm is efficient and suitable for smart devices. Experimental results validate efficiency of the proposed S2fM algorithm.

Pose estimation algorithm for mobile augmented reality based on inertial sensor fusion

“…With MEMS sensors becoming more accurate, the camera pose estimation problem turned down. An approach regarding the 3D camera rotation and translation which are the extrinsic parameters of camera  ISSN: 2088-8708 pose estimation, was made by [12], in which the vision data and inertial fusion using simplified structure from motion for pose estimation. A gyroscope sensor was used to estimate the camera rotation parameter, while the translation parameter was estimated separately.…”

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