  Finding the camera pose is an important step in many egocentric video
applications. It has been widely reported that, state of the art SLAM
algorithms fail on egocentric videos. In this paper, we propose a robust method
for camera pose estimation, designed specifically for egocentric videos. In an
egocentric video, the camera views the same scene point multiple times as the
wearer's head sweeps back and forth. We use this specific motion profile to
perform short loop closures aligned with wearer's footsteps. For egocentric
videos, depth estimation is usually noisy. In an important departure, we use 2D
computations for rotation averaging which do not rely upon depth estimates. The
two modification results in much more stable algorithm as is evident from our
experiments on various egocentric video datasets for different egocentric
applications. The proposed algorithm resolves a long standing problem in
egocentric vision and unlocks new usage scenarios for future applications.
