  Rolling Shutter (RS) cameras have become popularized because of low-cost
imaging capability. However, the RS cameras suffer from undesirable artifacts
when the camera or the subject is moving, or illumination condition changes.
For that reason, Monocular Visual Odometry (MVO) with RS cameras produces
inaccurate ego-motion estimates. Previous works solve this RS distortion
problem with motion prediction from images and/or inertial sensors. However,
the MVO still has trouble in handling the RS distortion when the camera motion
changes abruptly (e.g. vibration of mobile cameras causes extremely fast motion
instantaneously). To address the problem, we propose the novel MVO algorithm in
consideration of the geometric characteristics of RS cameras. The key idea of
the proposed algorithm is the new RS essential matrix which incorporates the
instantaneous angular and linear velocities at each frame. Our algorithm
produces accurate and robust ego-motion estimates in an online manner, and is
applicable to various mobile applications with RS cameras. The superiority of
the proposed algorithm is validated through quantitative and qualitative
comparison on both synthetic and real dataset.
