As computing power becomes more and more available, robotic systems are moving away from active sensors for environmental awareness and transitioning into passive vision sensors.  With the advent of teleoperation and real-time video tracking of dynamic environments, the need to stabilize video onboard mobile robots has become more prevalent.

This thesis presents a digital stabilization method that incorporates inertial fusion with a Kalman filter.  The camera motion is derived visually by tracking SIFT features in the video feed and fitting them to an affine model.  The digital motion is fused with a 3 axis rotational motion measured by an inertial measurement unit (IMU) rigidly attached to the camera. The video is stabilized by digitally manipulating the image plane opposite of the unwanted motion.

The result is the foundation of a robust video stabilizer comprised of both visual and inertial measurements.  The stabilizer is immune to dynamic scenes and requires less computation than current digital video stabilization methods.